Congressional Hearings
DIGITAL DECISION-MAKING: THE BUILDING BLOCKS OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Congressional Hearings
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Congress: Senate
CHRG-115shrg37295
| AUTHORITYID | CHAMBER | TYPE | COMMITTEENAME |
|---|---|---|---|
| sscm00 | S | S | Committee on Commerce, Science, and Transportation |
[Senate Hearing 115-649]
[From the U.S. Government Publishing Office]
S. Hrg. 115-649
DIGITAL DECISION-MAKING:
THE BUILDING BLOCKS OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
=======================================================================
HEARING
BEFORE THE
SUBCOMMITTEE ON COMMUNICATIONS, TECHNOLOGY, INNOVATION, AND THE
INTERNET
OF THE
COMMITTEE ON COMMERCE,
SCIENCE, AND TRANSPORTATION
UNITED STATES SENATE
ONE HUNDRED FIFTEENTH CONGRESS
FIRST SESSION
__________
DECEMBER 12, 2017
__________
Printed for the use of the Committee on Commerce, Science, and
Transportation
[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]
Available online: http://www.govinfo.gov
___________
U.S. GOVERNMENT PUBLISHING OFFICE
37-295 PDF WASHINGTON : 2019
SENATE COMMITTEE ON COMMERCE, SCIENCE, AND TRANSPORTATION
ONE HUNDRED FIFTEENTH CONGRESS
FIRST SESSION
JOHN THUNE, South Dakota, Chairman
ROGER F. WICKER, Mississippi BILL NELSON, Florida, Ranking
ROY BLUNT, Missouri MARIA CANTWELL, Washington
TED CRUZ, Texas AMY KLOBUCHAR, Minnesota
DEB FISCHER, Nebraska RICHARD BLUMENTHAL, Connecticut
JERRY MORAN, Kansas BRIAN SCHATZ, Hawaii
DAN SULLIVAN, Alaska EDWARD MARKEY, Massachusetts
DEAN HELLER, Nevada CORY BOOKER, New Jersey
JAMES INHOFE, Oklahoma TOM UDALL, New Mexico
MIKE LEE, Utah GARY PETERS, Michigan
RON JOHNSON, Wisconsin TAMMY BALDWIN, Wisconsin
SHELLEY MOORE CAPITO, West Virginia TAMMY DUCKWORTH, Illinois
CORY GARDNER, Colorado MAGGIE HASSAN, New Hampshire
TODD YOUNG, Indiana CATHERINE CORTEZ MASTO, Nevada
Nick Rossi, Staff Director
Adrian Arnakis, Deputy Staff Director
Jason Van Beek, General Counsel
Kim Lipsky, Democratic Staff Director
Chris Day, Democratic Deputy Staff Director
Renae Black, Senior Counsel
------
SUBCOMMITTEE ON COMMUNICATIONS, TECHNOLOGY, INNOVATION, AND THE
INTERNET
ROGER F. WICKER, Mississippi, BRIAN SCHATZ, Hawaii, Ranking
Chairman MARIA CANTWELL, Washington
ROY BLUNT, Missouri AMY KLOBUCHAR, Minnesota
TED CRUZ, Texas RICHARD BLUMENTHAL, Connecticut
DEB FISCHER, Nebraska EDWARD MARKEY, Massachusetts
JERRY MORAN, Kansas CORY BOOKER, New Jersey
DAN SULLIVAN, Alaska TOM UDALL, New Mexico
DEAN HELLER, Nevada GARY PETERS, Michigan
JAMES INHOFE, Oklahoma TAMMY BALDWIN, Wisconsin
MIKE LEE, Utah TAMMY DUCKWORTH, Illinois
RON JOHNSON, Wisconsin MAGGIE HASSAN, New Hampshire
SHELLEY CAPITO, West Virginia CATHERINE CORTEZ MASTO, Nevada
CORY GARDNER, Colorado
TODD YOUNG, Indiana
C O N T E N T S
----------
Page
Hearing held on December 12, 2017................................ 1
Statement of Senator Wicker...................................... 1
Letter dated December 11, 2017 to Hon. Roger Wicker and Hon.
Brian Schatz from Dean Garfield, President and CEO,
Information Technology Industry Council (ITI).............. 83
Letter dated December 12, 2017 to Hon. John Thune and Hon.
Bill Nelson from Marc Rotenberg, President, EPIC; Caitriona
Fitzgerald, Policy Director, EPIC; and Christine Bannan,
Policy Fellow, EPIC........................................ 84
Statement of Senator Schatz...................................... 2
Statement of Senator Moran....................................... 41
Statement of Senator Peters...................................... 44
Statement of Senator Udall....................................... 46
Statement of Senator Young....................................... 48
Statement of Senator Cantwell.................................... 50
Statement of Senator Markey...................................... 55
Statement of Senator Cruz........................................ 56
Statement of Senator Cortez Masto................................ 58
Statement of Senator Blumenthal.................................. 60
Witnesses
Dr. Cindy L. Bethel, Associate Professor, Department of Computer
Science and Engineering, Mississippi State University.......... 4
Prepared statement........................................... 5
Daniel Castro, Vice President, Information Technology and
Innovation Foundation (ITIF)................................... 8
Prepared statement........................................... 10
Victoria Espinel, President and CEO, BSA The Software
Alliance....................................................... 17
Prepared statement........................................... 18
Report entitled ``The $1 Trillion Economic Impact of Software 63
Dr. Dario Gil, Ph.D., Vice President, AI and IBM Q............... 26
Prepared statement........................................... 27
Dr. Edward W. Felten, Ph.D., Robert E. Kahn Professor of Computer
Science and Public Affairs, Princeton University............... 32
Prepared statement........................................... 34
Appendix
Response to written questions submitted to Dr. Cindy L. Bethel
by:
2Hon. Amy Klobuchar.......................................... 87
Hon. Tom Udall............................................... 87
Hon. Maggie Hassan........................................... 88
Response to written questions submitted to Daniel Castro by:
Hon. Tom Udall............................................... 90
Hon. Gary Peters............................................. 90
Hon. Maggie Hassan........................................... 91
Response to written questions submitted to Victoria Espinel by:
Hon. Gary Peters............................................. 92
Hon. Maggie Hassan........................................... 94
Response to written questions submitted to Dr. Dario Gil, Ph.D.
by:
Hon. Amy Klobuchar........................................... 96
Hon. Tom Udall............................................... 97
Hon. Gary Peters............................................. 98
Hon. Maggie Hassan........................................... 99
Response to written questions submitted to Dr. Edward W. Felten,
Ph.D. by:
Hon. Amy Klobuchar........................................... 102
Hon. Tom Udall............................................... 103
Hon. Gary Peters............................................. 103
Hon. Maggie Hassan........................................... 104
DIGITAL DECISION-MAKING:
THE BUILDING BLOCKS OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
----------
TUESDAY, DECEMBER 12, 2017
U.S. Senate,
Subcommittee on Communications, Technology,
Innovation, and the Internet,
Committee on Commerce, Science, and Transportation,
Washington, DC.
The Subcommittee met, pursuant to notice, at 10 a.m. in
room SR-253, Russell Senate Office Building, Hon. Roger Wicker,
Chairman of the Subcommittee, presiding.
Present: Senators Wicker [presiding], Schatz, Blunt, Cruz,
Fischer, Moran, Sullivan, Heller, Inhofe, Capito, Young,
Cantwell, Klobuchar, Blumenthal, Markey, Booker, Udall, Peters,
Hassan, and Cortez Masto.
OPENING STATEMENT OF HON. ROGER F. WICKER,
U.S. SENATOR FROM MISSISSIPPI
Senator Wicker. This hearing will come to order. Senator
Schatz will be here in a few moments and has sent word that we
should go ahead and proceed. Today's Subcommittee meets to
examine the commercial applications of artificial intelligence
and machine learning for the U.S. economy. We are also gathered
to discuss how the responsible design and deployment of
intelligent systems can foster innovation and investment,
propelling the United States as a leader in artificial
intelligence.
I'm glad to convene this hearing, and as I mentioned, my
colleague and friend Senator Schatz will be here in a moment.
Artificial intelligence refers to technology that is
capable of taking on human-like intelligence. Through data
inputs and algorithms, AI systems have the potential to learn,
to reason, to plan, perceive, process, make decisions, and even
act for themselves.
Although AI applications have been around for decades,
recent advancements, particularly in machine learning, have
accelerated in their capabilities because of the massive growth
in data gathered from billions of connected devices and the
digitization of everything. Developments in computer processing
technologies and better algorithms are also enabling AI systems
to become smarter and perform more unique tasks.
Every day consumers use technology that employ some degree
of AI, smartphone mapping apps that suggest faster driving
routes, for example. Online search platforms are learning from
past queries to generate increasingly customized results for
users. We all know that when we click on a site on the
Internet, news suggestions and advertisements on social media,
and semi-autonomous vehicles are just a few examples of how
machines and computer programs are taking on increasingly
cognitive tasks.
The excitement surrounding this technology is deserved. AI
has the potential to transform our economy, and so let's talk
about that today. AI's ability to process and sort through
troves of data can greatly inform human decisionmaking and
processes across industries, including agriculture, health
care, and transportation. In turn, businesses can be more
productive, profitable, and efficient in their operations.
As AI systems mature and become more accurate in their
descriptive, predictive, and prescriptive capabilities, there
are issues that should be addressed to ensure the responsible
development and use of this technology. Some of these issues
include: understanding how data is gathered, what data is
provided for an intelligent machine to analyze, and how
algorithms are programmed by humans to make certain
predictions. Moreover, understanding how the human end user
interacts with or responds to the digital decision, and how
humans interpret or explain decisions of the AI system over
time will also need to be addressed.
These are important considerations to ensure that the
decisions made by AI systems are based on representative data
that does not unintentionally harm vulnerable populations or
act in an unsafe, anticompetitive, or biased way. So there's a
lot to think about.
In addition to these issues, other considerations, such as
data privacy and cybersecurity, AI's impact on the workforce,
and human control and oversight over intelligence systems
should also be addressed as the technology develops.
Fundamental to the success of machine learning and AI in
enhancing U.S. productivity and empowering human decisionmaking
is consumer confidence and trust in these systems. To build
consumer confidence and trust, it is critical that the
integration of AI into our commercial and government processes
be done responsibly.
To that end, I look forward to learning from today's
witnesses about how AI is advancing in our economy and what
best practices our industry and AI researchers are considering
to achieve all the promised economic and societal benefits of
this technology.
Senator Schatz, do you have anything to add to my
comprehensive opening statement?
STATEMENT OF HON. BRIAN SCHATZ,
U.S. SENATOR FROM HAWAII
Senator Schatz. I think everything has been said, but not
everybody has said it. So good morning. Thank you very much,
Mr. Chairman.
AI is advancing fast. Each year, processing power gets
better, hardware gets cheaper, and algorithms are easier to
train thanks to bigger and better datasets. These advances are,
for companies and economies, great opportunities.
Technologists, historians, and economists say that we're at
the cusp of the next industrial revolution, but there are
concerns. We've seen that AI can be a black box. It can make
decisions and come to conclusions without showing its
reasoning. There are also known cases of algorithms that
discriminate against minority groups, and when you start to
apply these systems to criminal justice, health care, or
defense, the lack of transparency and accountability is
worrisome.
Given the many concerns in a field that's advancing so
quickly and is so revolutionary, it's hard to believe that
there is no AI policy at the Federal level, and that needs to
change. To start, the government should not purchase or use AI
systems if we can't explain what it does, especially if these
systems are making decisions about our citizens' lives.
There also needs to be more transparency and consumer
control on data collection. Too many consumers still do not
know what data is being collected, how it's being collected, or
who owns it. Some of our current laws and regulations work, but
some of them are too old and outdated to be used as a strong
foundation for AI.
For example, companies often use data scraping to build
their AI models. This falls under the Computer Fraud and Abuse
Act, a 1986 law that was written before the Web was really in
operation. AI is now used to write news articles, edit
photographs, artificially reconstruct movies, all actions that
fall under the Digital Millennium Copyright Act, which was
passed in 1998, 10 years before the iPhone.
Our laws used to apply to actions in the physical world,
but now they apply to software systems that ultimately do the
same thing. I'm glad to see that industry and academia are
being proactive by coming up with policy, principles, and
professional ethics codes, but with this kind of patchwork, the
system is only as strong as its weakest link.
From the private sector to academia to government, everyone
has to wrestle with the ethical and policy questions that AI
raises. And that's why I intend to introduce a bill creating an
independent Federal commission to ensure that AI is adopted in
the best interest of the public. If created, the commission
would serve as a resource and coordinating body to the 16-plus
Federal agencies that govern the use of AI. Otherwise, we risk
bureaucratic turf wars and outdated inconsistent rules.
The commission would also be tasked with asking the tough
ethical questions around AI. For instance, employers may not
legally ask interviewees about their religion, marital status,
or race, but they use software that mines social media data
that may make the same inferences. Judges cannot base their
sentencing decisions on whether the defendant has family
members in jail, yet these facts contribute to a risk score
calculated by machine learning algorithms.
In these cases, it's not clear yet where we draw the line
on what is legal and what is not. In some instances, existing
statutes will suffice. A lot of our laws actually work just
fine when it comes to AI, and great laws can survive the test
of time. But there are a few things that need to be wrestled
with today in Congress and with our agencies, and that's why
this hearing is so important.
Thank you very much. I look forward to hearing the
testimony.
Senator Wicker. Thank you, Senator Schatz.
Our witnesses today are Dr. Cindy Bethel, Associate
Professor, Department of Computer Science and Engineering at
Mississippi State University; Mr. Daniel Castro, Vice
President, Information Technology and Innovation Foundation;
Ms. Victoria Espinel, Chief Executive Officer, BSA-The Software
Alliance; Dr. Dario Gil, Vice President, IBM Research, AI, and
IBM Q; and Dr. Edward Felten, Robert E. Kahn Professor of
Computer Science and Public Affairs, Princeton University.
Friends, we will begin on my left with Dr. Bethel and
proceed with 5-minute opening statements down the table. Thank
you very much.
Dr. Bethel.
STATEMENT OF DR. CINDY L. BETHEL, ASSOCIATE
PROFESSOR, DEPARTMENT OF COMPUTER SCIENCE
AND ENGINEERING, MISSISSIPPI STATE UNIVERSITY
Dr. Bethel. Good morning, Chairman Wicker, Ranking Member
Schatz, and members of the Committee. Thank you for the
opportunity to appear before you today. I'm Dr. Cindy Bethel.
I'm Associate Professor of Computer Science and Engineering at
Mississippi State University, or MSU.
It is an honor to speak with the Committee today about
digital decisionmaking associated with artificial intelligence,
known as AI, from the academic perspective, and about
applications of AI being developed at MSU.
Today I will address three primary areas: first, a brief
introduction to AI; next, three AI projects being developed at
MSU; and last I will discuss some key points associated with
AI.
A critical aspect associated with the advancement of
science and technology is the development of algorithms with AI
including machine learning for digital decisionmaking. In order
for a system to make a decision, it must first acquire
information, process and learn from that information, and then
use that information to make decisions.
The gold standard would be the ability for a system to make
a decision in a manner similar to a human expert in that area.
This is a relatively new field of science that will be--provide
ongoing research opportunities, including efforts to enhance
existing algorithms and develop new and more efficient and
effective algorithms. This is critical to the overall
advancement of science in various disciplines, such as
robotics, medicine, economics, and others.
There are countless application areas for which AI is
beneficial and may make a significant impact on society. At
MSU, we are on the forefront of AI developments with our
research efforts, and today I will focus on three of those--
excuse me--projects.
First is the integration of robots into high-risk law
enforcement operations, such as SWAT teams, who I've trained
with monthly for the last 6 years. This is an application for
highly dynamic situations that require officers to make life-
critical decisions. The more information they have prior to
entering these dangerous and unknown environments, the better
decisions the officers can make.
We are developing algorithms using robots into an
environment prior to entry to provide critical audio and video
intelligence to allow officers to make more informed decisions.
This information continues the dynamics of how they make entry
or process a scene. Our research through intelligent interfaces
will help inform how this information is delivered to the
officers and maximize safety and performance.
Second, Mississippi State University is researching the
development of autonomous cargo transport systems used in the
fast-paced dynamic environment of a top 100 logistics company.
This involves choosing the proper sensors to ensure that data
is able to make--help make informed decisions on the vehicle's
path.
Further, we are researching a variety of human factors
because controlled vehicles will transfer from autonomous to
human driver control and back again. Our work shows that if a
human is not actively engaged in driving, they may--there may
be insufficient situational awareness to control--take control
on short notice. So how the human driver will be notified of
the transfer of control is another critical aspect of our work.
To ensure maximum safety, researchers at MSU are exploring
situations in which humans are operating in close proximity to
the autonomous vehicles, such as a vehicle that's docking to
deliver cargo, and a worker is unloading and loading this
cargo.
Finally, another one of MSU's AI research projects involves
a robotic therapy support system, known as Therabot that is in
the form of a stuffed robotic dog. Therabot is an alternative
to animal-assisted therapy for people who may be allergic to
animals or may not be able to care for a live animal. Therabot
will be used to provide support during clinical therapy
sessions and home therapy practice with children or adults who
are dealing with post-traumatic stress disorders or other
mental health concerns. The algorithms being developed modify
the autonomous behaviors of the robot based on the interactions
with the human to accommodate his or her preferences and in
response to different levels of stress detected by the robot,
and to provide improved comfort and support.
AI will only be as good as the data the system receives and
its ability to process that information to make a decision. The
better the quality and quantity of information available to the
system, the better the results will be from the machine
learning process, which results in better final decisions from
the system. Otherwise, decisionmaking capabilities can be
limited or inaccurate.
The potential applications of AI are almost limitless. The
United States can and should remain at the forefront of AI
research, application development, and innovation provided that
government proceeds with a light regulatory touch that doesn't
stifle this potential.
Thank you so much for the opportunity to testify today on
these important topics. I appreciate your time and attention to
the advancement and impact of digital decisionmaking and AI.
[The prepared statement of Dr. Bethel follows:]
Prepared Statement of Dr. Cindy L. Bethel, Associate Professor of
Computer Science and Engineering; Director of the Social, Therapeutic,
and Robotic Systems (STaRS) Lab; Billie J. Ball Endowed Professor in
Engineering; Mississippi State University
Chairman Wicker, Ranking Member Schatz, and Members of the
Committee, thank you for the opportunity to appear before you today. I
am Dr. Cindy Bethel, an Associate Professor of Computer Science and
Engineering, the Billie J. Ball Endowed Professor in Engineering, and
the Director of the Social, Therapeutic, and Robotic Systems (STaRS)
Lab at Mississippi State University. It is an honor to speak with the
committee today about digital decision-making associated with
artificial intelligence (AI) from the academic perspective and about
applications of AI being developed at Mississippi State University.
A critical aspect associated with the advancement of science and
technology is the development of algorithms associated with AI
including machine learning for digital decision-making. In order for a
system to make a decision it must acquire information, have a means of
processing and learning from that information, and have the ability to
use that information to make an informed decision. The gold standard
would be the ability for a machine or system to make a decision in a
manner similar to that of a human who is an expert in that area. We
have made considerable progress toward this goal since the inception of
what is considered artificial intelligence, which began in 1943 with
the research performed by McCulloch and Pitts. Today many machines and
systems rely upon the use of artificial intelligence for digital
decision-making. This is a relatively new field of science that will
provide many lifetimes of research including efforts to enhance
existing algorithms and develop new, more efficient, and effective
algorithms. This is critical to the overall advancements of science in
many disciplines, such as robotics, medicine, economics, and many
others. There are often disagreements in the field as to what is
considered AI and what algorithms and techniques used for learning are
considered the best.
There are many application areas for which artificial intelligence
is beneficial and may make a significant impact on society. At
Mississippi State University, we are actively conducting AI research
with several research projects that use AI and machine learning
techniques, but I will focus on three primary projects.
The first project is the integration of robots into law enforcement
operations, especially high risk, life critical incident responses such
as those used with special weapons and tactics (SWAT) teams. I have
been training monthly with rural regional SWAT team members since 2011.
This is an example of an application in which high risk, dynamic
situations are encountered, that require often officers to make life-
critical decisions. The more information or intelligence they have
prior to entering these dangerous and unknown environments, the better
decisions the officers can make. We are investigating and developing
algorithms related to computer vision, sensor fusion, and scene
understanding to send a robot in prior to entry to provide audio and
video feedback to officers during a response, highlighting what is
critical information for them to attend to so that they are not
overwhelmed with information when under high stress. The algorithms
identify what is important to the officers in the environment, such as
children, weapons, and other possible threats. This information can
change the dynamics of how they make entry or process the scene. We are
also researching in what ways this information needs to be provided to
the officers, so that they can use it to their advantage to keep them
and others safer in the performance of their duties. For example if
officers are conducting a slow and methodical search of a building for
a suspect, and the environment is quiet, dark and threat is nearby,
they would not want to receive the information in a openly visual
manner such as a video stream on a mobile phone that would highlight
them in the environment and make them more likely to be the target of
harm. In this case, they may want a verbal description of the scene
that comes across on their radio earpiece. If they are in a gunfight or
if there is an alarm that is sounding in the environment, but they are
in a relatively ``safe'' location, then they may want to receive this
information in a visual form, because audio transmission would be
difficult to hear. We are researching the development of intelligent
interface switching in which the manner that information is delivered
to the officers may change depending on what is happening in the
environment they are operating in. The officers are excited and ready
to start deploying some of these artificial intelligence and machine
learning applications in real-world responses.
A second project that we are working on at MSU is the development
of autonomous cargo transport systems to be used in the fast-paced,
dynamic environment of a top 100 logistics company. A primary factor
that needs to be considered is what sensors need to be used to make
informed decisions on the path the vehicles must travel. We also need
to consider humans, who are sitting in the driver's seat of these
vehicles, because control of the vehicles will change between fully
autonomous to human driver operated. Research has shown that if the
human is not actively involved in the activity of driving, that there
may not be adequate situation awareness to be able to take back control
of the vehicle with relatively short notice if needed. This has
potential for life critical decision-making. We are investigating how
the system needs to be able to alert the driver that control is being
transferred, either to the vehicle or back to the human driver.
It is also important to consider the types of notifications that
need to occur to ensure safety and situational awareness of what is
happening around the vehicle. Also there are situations in which humans
are operating in close proximity to the autonomous vehicle. As human
drivers, we observe consciously or unconsciously behaviors of other
drivers to infer what nearby vehicles will do next, but if there are
not those cues, then how do humans in the environment understand what
the vehicle or system will do next? This is a major issue of concern.
This occurs also when the vehicle is docking to deliver cargo and a
human is involved in unloading and loading this cargo. We are exploring
methods of notification to the person about what the vehicle will do
next. This is of significant concern, and if the incorrect decision is
made, the vehicle could cause harm to the human.
The third project involves a robotic therapy support system, known
as TherabotTM that is in the form of a stuffed robotic dog.
TherabotTM is an alternative to animal-assisted therapy, for
people who may be allergic to animals or may not be able to care for a
live animal. TherabotTM will be used to provide support
during clinical therapy sessions and for home therapy practice with
children or adults who are dealing with post-traumatic stress disorders
and other mental health concerns. The algorithms being developed for
this project modify the autonomous behaviors and responses of the robot
based on the interactions with the human to accommodate his or her
preferences and in response to different levels of stress detected.
Machine learning is being used to understand what behaviors the user
prefers and to provide better support. It allows TherabotTM
to be customizable to each individual user. It will learn and respond
to each user as a dog would each person it encounters. Currently,
TherabotTM can detect different types of touch, such as
petting, patting, and hugging and will respond in different ways. If a
person is under stress during the interaction he or she may squeeze the
robot and the robot will adapt its behaviors to provide more comfort
and support.
Algorithms used in the research and development of systems that are
capable of digital decision-making are being developed and enhanced by
researchers all across the world. Mississippi State University is at
the forefront of these research developments and is continually
contributing through publications and sharing of knowledge, algorithms,
and software developments.
Research as a whole involves exploring what others have performed
and then determining if there are modifications that can be made to
improve upon those algorithms or the development of new algorithms to
meet the needs of the application of use. For example, algorithms are
being developed to learn a user's preference for how close a robot
stands to them and still feel comfortable, or which friend they like
most on a social media account. There are many methods for solving a
problem. I typically tell my students who are interested in pursuing
research that ``you need to be first or best at something and it is
always best to be first.''
Artificial intelligence will only be as good as the data the system
receives and its ability to process that information to make a
decision. This is a critical aspect to the advancement of this field
that can impact almost any other discipline. The system or machine must
have the ability to perceive information and that typically comes from
sensors and other forms of data. There are many types of sensing
systems such as a camera, streaming video, thermal images based on the
heat signature of items in the environment, radar, and many others.
There are also methods of gathering information from sources such as
social media, mobile device location history, purchase history, product
preferences, websites visited, etc., that can assist in the decision-
making process. The better the quality and quantity of information
available to the system, the better the results will be from the
machine learning process, which results in a better final decision from
the system.
Algorithms are programmed to receive data as an input, process that
data, learn from large amounts of data, and then use that information
to make a digital decision. If there is not sufficient amounts of data
available to train the machine learning algorithms or enough diversity
in the data to allow the learning algorithms to adapt to different
aspects, then the decision-making capabilities can be limited or
inaccurate.
Another major issue of concern is the processing power necessary to
handle large amounts of information received and come to a decision.
This is especially a concern on smaller systems such as robots that
have limited onboard processing capabilities. Many of the AI problems
that are being addressed in the research community are performed on
high powered computing resources and simulations are performed to
validate the results. This is fine for many scientific applications,
but in order for AI and machine learning to be beneficial in real-world
applications, it will be necessary to perform the decision-making
processes in real-time. The results need to be made available in an
instant and not have to wait for processing time to provide a result.
This is improving, but sensing and processing are currently significant
limitations to the application and use of AI in digital decision-
making.
The level of human engagement necessary for digital decision-making
depends on the state of the AI system. There are different levels of
autonomous decision making. There is full autonomy, where the system
receives the input and then processes the information from that data
and makes a decision with no input from a human. There is supervised
autonomy, in which the system receives information, processes the data,
and comes up with possible results, and the human may have the ability
to override or make the final decision. The more common level of
autonomy is supervised autonomy. The level of human engagement also
needs to consider the ramifications of the decision-making process. If
it is a life-critical decision, then most people are more comfortable
with a human remaining involved in the process to ensure an ethical and
``good'' decision is the final result. There are many ethical hurdles
that will need to be decided at some point as to who is responsible if
an AI system makes an incorrect decision, especially if the decision
could result in harm to humans. There have been discussions in the
field among researchers regarding who is responsible for this decision,
such as the programmer, the company that made the system, or others.
The current state often requires a human to be involved at some level
of the final decision-making process unless it is low risk or well
validated that the system will always make a ``right'' decision.
The fields of artificial intelligence and machine learning are at
such an early stage of scientific development that standards and best
practices are discussed among researchers; however, there is not a
single standard or set of best practices that I am aware of that all
researchers and scientists follow at this point. The biggest concern is
providing the best possible answers that will not result in harm and
provides benefits to the users.
The algorithms developed for machine learning and artificial
intelligence can be used in almost any area of research, development,
and discipline. It can be used to improve the decision-making process
of humans. The processing of some information by a computer can be
faster than what can be achieved by a human brain. Almost any aspect of
society can benefit from the use of high quality artificial
intelligence capabilities.
A critical aspect in the development of artificial intelligence
using machine learning and other techniques is the impact on the humans
that are involved. A current limitation to the advancement of
artificial intelligence is the quality and cost effectiveness of
sensing capabilities to provide high quality information or data to the
system to make those digital decisions. Another critical limitation to
current artificial intelligence capabilities is onboard processing
capabilities and the cost effectiveness of those systems. We have come
a long way in the advancement of artificial intelligence; however we
still have a long way to go! The potential applications of AI are
almost limitless. The United States can and should remain at the
forefront of AI research, application development, and innovation
provided that the government proceeds with a light regulatory touch
that doesn't stifle this potential.
Thank you so much for the opportunity to testify today on these
important topics. I appreciate your time and attention to the
advancement and impacts of digital decision-making and artificial
intelligence.
Senator Wicker. Thank you, Dr. Bethel. Precisely 5 minutes.
Dr. Bethel. Thank you.
[Laughter.]
Senator Wicker. Mr. Castro, we're delighted to have you.
STATEMENT OF DANIEL CASTRO, VICE PRESIDENT,
INFORMATION TECHNOLOGY AND INNOVATION FOUNDATION (ITIF)
Mr. Castro. Thank you. Chairman Wicker, Ranking Member
Schatz, and members of the Committee, I appreciate the
invitation to be here today.
AI has the potential to create a substantial and lasting
impact on the economy by increasing the level of automation in
virtually every sector, leading to more efficient processes and
higher quality outputs, and boosting productivity in per capita
incomes.
In the coming years, AI is expected to generate trillions
of dollars of economic value and help businesses make smarter
decisions, develop innovative products and services, and boost
productivity. For example, manufacturers are using AI to invent
new metal alloys for 3D printing, pharmaceutical companies are
using AI to discover new lifesaving drugs, and agricultural
businesses are using AI to increase automation on farms.
Companies that use AI will have an enormous advantage
compared to their peers that do not; therefore, the United
States should prioritize policy initiatives that promote AI
adoption in its traded sectors where U.S. firms will face
international competition.
Many other countries already see the strategic importance
of becoming lead adopters of AI, and they have begun
implementing policies to pursue this goal. For example, this
past March, Canada launched the Pan-Canadian AI Strategy, which
is intended to help Canada become an international leader in AI
research. The U.K.'s new budget, which was published last
month, includes several provisions that have the goal of making
the U.K. a world leader in AI, including establishing a new
research center and funding about 500 Ph.D.'s. Japan has
created an AI technology strategy designed to develop and
commercialize AI in a number of fields, including
transportation and health care. And China has declared its
intent to be the world's premier AI innovation center by 2030.
However, to date, the U.S. Government has not declared its
intent to be a global leader in this field, nor has it begun
the even harder task of developing a strategy to achieve that
vision. Moreover, China, which has launched this ambitious
program to dominate the field, has already surpassed the United
States in terms of the total number of papers published and
cited in some AI disciplines, such as deep learning.
The U.S. should not cede its existing advantages in AI.
Instead, it should pursue a multipronged national strategy to
remain competitive in this field. First, the Federal Government
should continue to expand its funding to support strategic
areas of AI, especially in areas industry is unlikely to invest
in, as well as better plan and coordinate Federal funding for
AI R&D across different agencies.
Second, the Federal Government should support educational
efforts to ensure a strong pipeline of talent to create the
next generation of AI researchers and developers, including the
retraining and diversity programs as well as pursue integration
policies that allow U.S. businesses to recruit and retain
highly skilled computer scientists.
Third, Federal and state regulators should conduct
regulatory reviews to identify regulatory barriers to
commercial use of AI in various industries, such as
transportation, health care, education, and finance.
Fourth, the Federal Government should continue to supply
high-value datasets that enable advances in AI, such as
providing open access to standardized reference datasets for
text analysis and facial recognition. Federal agencies should
also facilitate data-sharing between industry stakeholders just
as the Department of Transportation has done on safety for
autonomous vehicles.
And, fifth, the Federal Government should assess what types
of economic data it needs to gather from businesses to monitor
and evaluate AI adoption, much like it tracked rural
electrification or broadband connectivity as key economic
indicators.
Now, as with any technology, there will be some risks and
challenges associated with AI that require government
oversight, but the U.S. should not replicate the European
approach to AI, where rules creating a right to explanation and
a right to human review for automated decisions risk severely
curtailing the uses of AI.
Instead, the U.S. should create its own innovation-friendly
approach to providing oversight of the emerging algorithmic
economy just as it has for the Internet economy. Such an
approach should prioritize sector-specific policies over
comprehensive regulation, outcomes over transparency, and
enforcement actions against firms that cause tangible harm over
those that merely make missteps without injury.
In many cases, regulators will not need to intervene
because the private sector will address problems about AI, such
as bias or discrimination, on its own. Moreover, given that
U.S. companies are at the forefront of efforts to build AI that
is safe and ethical, maintaining U.S. leadership in this field
will be important to ensure these values remain embedded in
this technology.
AI is a transformational technology that has the potential
to significantly increase efficiency and innovation across the
U.S. economy, creating higher living standards and improve
quality of life. But while the United States has an early
advantage in AI, many other countries are trying to be number
one--they're trying to be number one. We need more leadership
on this issue. And I look forward to working with any member of
the Committee on their proposed legislation and new ideas in
this space. And I commend you all for holding this hearing.
Thank you for the opportunity to be here today. And I look
forward to the questions.
[The prepared statement of Mr. Castro follows:]
Prepared Statement of Daniel Castro, Vice President,
Information Technology and Innovation Foundation (ITIF)
Introduction
Chairman Wicker, Ranking Member Schatz and members of the
subcommittee, I appreciate the opportunity to appear before you to
discuss the importance of artificial intelligence (AI) to the U.S.
economy and how best to govern this important technology. My name is
Daniel Castro, and I am vice president of the Information Technology
and Innovation Foundation (ITIF), a non-profit, nonpartisan think tank
whose mission is to formulate and promote public policies to advance
technological innovation and productivity, and director of ITIF's
Center for Data Innovation.
What is Artificial Intelligence?
AI is a field of computer science devoted to creating computer
systems that perform tasks much like a human would, particularly tasks
involving learning and decision-making.\1\ AI has many functions,
including, but not limited to:
---------------------------------------------------------------------------
\1\ Daniel Castro and Joshua New, ``The Promise of Artificial
Intelligence,'' Center for Data Innovation, October 2016, http://
www2.datainnovation.org/2016-promise-of-ai.pdf.
Learning, which includes several approaches such as deep
learning (for perceptual tasks), transfer learning,
---------------------------------------------------------------------------
reinforcement learning, and combinations thereof;
Understanding, or deep knowledge representation required for
domain-specific tasks, such as medicine, accounting, and law;
Reasoning, which comes in several varieties, such as
deductive, inductive, temporal, probabilistic, and
quantitative; and
Interacting, with people or other machines to
collaboratively perform tasks, and for interacting with the
environment.
The cause of many misconceptions about AI, particularly its
potential harms, is that some people conflate two very distinct types
of AI: narrow AI and strong AI. Narrow AI describes computer systems
adept at performing specific tasks, but only those specific types of
tasks--somewhat like a technological savant.\2\ For example, Apple's
Siri virtual assistant is capable of interpreting voice commands, but
the algorithms that power Siri cannot drive a car, predict weather
patterns, or analyze medical records. While other algorithms exist that
can accomplish those tasks, they too are narrowly constrained--the AI
used for an autonomous vehicle will not be able predict a hurricane's
trajectory or help doctors diagnose a patient with cancer.
---------------------------------------------------------------------------
\2\ Irving Wladawksy-Berger, `` `Soft' Artificial Intelligence Is
Suddenly Everywhere,'' The Wall Street Journal, January 16, 2016,
http://blogs.wsj.com/cio/2015/01/16/soft-artificial-intelligence-is-
suddenly-everywhere/.
---------------------------------------------------------------------------
In contrast, strong AI, also referred to as artificial general
intelligence (AGI), is a hypothetical type of AI that can meet or
exceed human-level intelligence and apply this problem-solving ability
to any type of problem, just as the human brain can easily learn how to
drive a car, cook food, and write code.\3\ Many of the dystopian fears
about AI--that it will eliminate most jobs or go out of control and
wipe out humanity, for example--stem from the notion that AGI is
feasible, imminent, and uncontrollable.\4\ However, at least for the
foreseeable future, computer systems that can fully mimic the human
brain are only going to be found in scripts in Hollywood, and not labs
in Silicon Valley.
---------------------------------------------------------------------------
\3\ Ibid.
\4\ Robert D. Atkinson, ``'It's Going to Kill Us!' and Other Myths
About the Future of Artificial Intelligence,'' (Information Technology
and Innovation Foundation, June 2016), http://www2.itif.org/2016-myths-
machine-learning.pdf?_ga=1.201838291.334601971.1460947053.
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The application of AI has seen a surge in recent years because of
the development of machine learning--a branch of AI that focuses on
designing algorithms that can automatically and iteratively build
analytical models from data without needing a human to explicitly
program the solution. Before machine learning, computer scientists had
to manually code a wide array of functions into a system for it to
mimic intelligence. But now developers can achieve the same, or better,
results more quickly and at a lower cost using machine learning
techniques. For example, Google uses machine learning to automatically
translate content into different languages based on translated
documents found online, a technique that has proven to be much more
effective than prior attempts at language translation.\5\
---------------------------------------------------------------------------
\5\ Pedro Domingos, The Master Algorithm: How the Quest for the
Ultimate Learning Machine Will Remake Our World (New York: Basic Books,
2015).
---------------------------------------------------------------------------
What Are the Potential Benefits of AI?
AI will have a substantial and lasting impact on the economy by
increasing the level of automation in virtually every sector, leading
to more efficient processes and higher-quality outputs, and boosting
productivity and per-capita incomes. For example, the McKinsey Global
Institute estimates that by 2025 automating knowledge work with AI will
generate between $5.2 trillion and $6.7 trillion of global economic
value, advanced robotics relying on AI will generate between $1.7
trillion and $4.5 trillion, and autonomous and semi-autonomous vehicles
will generate between $0.2 trillion and $1.9 trillion.\6\ Deloitte
estimates that the Federal Government could save as much as $41.1
billion annually by using AI to automate tasks.\7\ And Accenture
predicts that by 2035, AI could increase the annual growth rate of the
U.S. economy by 2 percentage points, the Japanese economy by 1.9, and
the German economy by 1.6.\8\ The report also found that, for the 12
countries surveyed, AI would boost labor productivity rates by 11 to 37
percent.\9\
---------------------------------------------------------------------------
\6\ James Manyika et al., Disruptive Technologies: Advances That
Will Transform Life, Business, and the Global Economy,'' (McKinsey
Global Institute, May 2013), http://www.mckin
sey.com/business-functions/business-technology/our-insights/disruptive-
technologies.
\7\ Peter Viechnicki and William D. Eggers, ``How much time and
money can AI save government?'' (Deloitte, April 26, 2017), https://
dupress.deloitte.com/dup-us-en/focus/cognitive-technologies/artificial-
intelligence-government-analysis.html.
\8\ Mark Purdy and Paul Daugherty, ``Why Artificial Intelligence Is
the Future of Growth,'' (Accenture, September 28, 2016), https://
www.accenture.com/us-en/_acnmedia/PDF-33/Accen
ture-Why-AI-is-the-Future-of-Growth.pdf.
\9\ Ibid.
---------------------------------------------------------------------------
There are a vast and diverse array of uses for AI, and many U.S.
businesses are already using the technology today. Manufacturers are
using AI to invent new metal alloys for 3D printing; pharmaceutical
companies are using AI to discover new lifesaving drugs; mining
companies are using AI to predict the location of mineral deposits; and
agricultural businesses are using AI to increase automation on farms.
The International Data Corporation (IDC) estimates that the market for
AI technologies that analyze unstructured data will reach $40 billion
by 2020.\10\ And AI startups have attracted significant investment,
with U.S. investors putting $757 million in venture capital in AI
start-ups in 2013, $2.18 billion in 2014, and $2.39 billion in
2015.\11\
---------------------------------------------------------------------------
\10\ ``Cognitive Systems Accelerate Competitive Advantage,'' IDC,
accessed September 29, 2016, http://www.idc.com/promo/thirdplatform/
innovationaccelerators/cognitive.
\11\ ``Artificial Intelligence Explodes: New Deal Activity Record
for AI Startups,'' CB Insights, June 20, 2016, https://
www.cbinsights.com/blog/artificial-intelligence-funding-trends/.
---------------------------------------------------------------------------
In some cases, the principle benefit of AI is that it automates
work that would otherwise need to be performed by a human, thereby
boosting efficiency. Sometimes AI can complete tasks that it is not
always worth paying a human to do but still creates value, such as
writing newspaper articles to summarize Little League games.\12\ In
other cases, AI adds a layer of analytics that uncovers insights human
workers would be incapable of providing on their own, thereby boosting
quality. In some cases, it does both. For example, researchers at
Stanford have used machine learning techniques to develop software that
can analyze lung tissue biopsies with significantly more accuracy than
a top human pathologist and at a much faster rate.\13\ By analyzing
large volumes of data, researchers can train their computer models to
reliably recognize known indicators of specific cancer types as well as
discover new predictors.
---------------------------------------------------------------------------
\12\ Steven Levy, ``Can an Algorithm Write a Better News Story Than
a Human Reporter?'' Wired, April 24, 2012, https://www.wired.com/2012/
04/can-an-algorithm-write-a-better-news-story-than-a-human-reporter/.
\13\ Kun-Hsing Yu et al., ``Predicting non-small cell lung cancer
prognosis by fully automated microscopic pathology image features,''
Nature, August 16, 2017, https://www.nature.com/articles/ncomms12474.
---------------------------------------------------------------------------
AI is also delivering valuable social benefits, such as by helping
authorities rapidly analyze the deep web to crack down on human
trafficking, fighting bullying and harassment online, helping
development organizations better target impoverished areas, reducing
the influence of gender bias in hiring decisions, and more.\14\ Just as
AI can help businesses make smarter decisions, develop innovative new
products and services, and boost productivity to drive economic value,
it can achieve similar results for organizations generating social
value, and many of these solutions have the potential to scale
globally.
---------------------------------------------------------------------------
\14\ Larry Greenemeier, ``Human Traffickers Caught on Hidden
Internet,'' Scientific American, February 8, 2015 http://
www.scientificamerican.com/article/human-traffickers-caught-on-hidden-
internet/; Davey Alba, ``Weeding Out Online Bullying Is Tough, So Let
Machines Do It,'' Wired, July 10, 2015, https://www.wired.com/2015/07/
weeding-online-bullying-tough-let-machines/; Michelle Horton,
``Stanford Scientists Combine Satellite Data, Machine Learning to Map
Poverty,'' Stanford News, August 18, 2016 http://news.stanford.edu/
2016/08/18/combining-satellite-data-machine-learning-to-map-poverty/;
Sean Captain, ``How Artificial Intelligence is Finding Gender Bias at
Work,'' Fast Company, October 10, 2015, https://www.fastcompany.com/
3052053/elasticity/how-artificial-intelligence-is-finding-gender-bias-
at-work.
---------------------------------------------------------------------------
Finally, AI will be an increasingly important technology for
defense and national security. AI can address many goals, such as
improving logistics, detecting and responding to cybersecurity
incidents, and analyzing the enormous volume of data produced on the
battlefield. Moreover, AI will be a core enabler of the Pentagon's
``Third Offset Strategy,'' a policy designed to keep the United States
ahead of adversaries, especially ones capable of fielding numerically
superior forces, through technological superiority.\15\ Indeed, one top
Pentagon general has suggested that the Defense Department should never
buy another weapons system that does not have AI built into it.\16\
---------------------------------------------------------------------------
\15\ Sydney Freedberg, ``Faster Than Thought: DARPA, Artificial
Intelligence, & The Third Offset Strategy,'' Breaking Defense, February
11, 2016, https://breakingdefense.com/2016/02/faster-than-thought-
darpa-artificial-intelligence-the-third-offset-strategy/.
\16\ Jack Corrigan, ``Three-Star General Wants Artificial
Intelligence in Every New Weapon System,'' Nextgov, November 2, 2017,
http://www.nextgov.com/cio-briefing/2017/11/three-star-general-wants-
artificial-intelligence-every-new-weapon-system/142225/.
---------------------------------------------------------------------------
How Should Policymakers Support the Adoption and Use of AI?
Given the potential economic impact of AI in raising productivity,
policymakers should develop a national strategy to support the
development and adoption of AI in U.S. businesses. In particular, given
the enormous advantage that AI-enabled firms will have compared to
their non-AI-enabled peers, the United States should focus on AI
adoption in its traded sectors where U.S. firms will face international
competition. Many other countries see the strategic importance of
becoming lead adopters of AI, and they have begun implementing policies
to pursue this goal. These include:
Canada: In March 2017, Canada launched the Pan-Canadian
Artificial Intelligence Strategy which sets a goal of
establishing Canada as an international leader in AI research.
The strategy has four goals, which include increasing the
number of AI researchers and graduates; establishing three
major AI research centers; developing global thought leadership
on the economic, ethical, policy and legal implications of
advances in AI; and supporting the national AI research
community.\17\
---------------------------------------------------------------------------
\17\ ``Pan-Canadian Artificial Intelligence Strategy Overview,''
Canadian Institute for Advanced Research, March 3, 2017, https://
www.cifar.ca/assets/pan-canadian-artificial-intelligence-strategy-
overview/.
China: China's State Council issued a development plan for
AI in July 2017 with the goal of making China a leader in the
field by 2030. China's goal is to be equal to countries
currently leading in AI by 2020. Then, over the subsequent five
years, China will focus on developing breakthroughs in areas of
AI that will be a ``a key impetus for economic
transformation.'' \18\ Finally, by 2030, China intends to be
the world's ``premier artificial intelligence innovation
center.'' \19\ China's plan also signals its intent to require
high school students to take classes in AI, which is one of the
most ambitious efforts to develop human capital for the AI
economy of any nation.
---------------------------------------------------------------------------
\18\ Graham Webster et al., ``China's Plan to `Lead' in AI:
Purpose, Prospects, and Problems,'' New America Foundation, August 1,
2017, https://www.newamerica.org/cybersecurity-initiative/blog/chinas-
plan-lead-ai-purpose-prospects-and-problems/.
\19\ Ibid.
Japan: Prime Minister Abe launched the Artificial
Intelligence Technology Strategy Council in April 2016 to
develop a roadmap for the development and commercialization of
AI.\20\ Published in May 2017, the roadmap outlines priority
areas for research and development (R&D), focusing on the
themes of productivity, mobility, and health. The strategy also
encourages collaboration between industry, government, and
academia to advance AI research, as well as stresses the need
for Japan to develop the necessary human capital to work with
AI. Japan also launched its Japan Revitalization Strategy 2017,
which details how the government will work to support growth in
certain areas of the economy. The 2017 strategy includes a push
to promote the development of AI for telemedicine and self-
driving vehicles to address the shortage of workers in Japan.
---------------------------------------------------------------------------
\20\ Josh New, ``How Governments Are Preparing for Artificial
Intelligence,'' August 8, 2017, https://www.datainnovation.org/2017/08/
how-governments-are-preparing-for-artificial-intelligence/.
UK: The United Kingdom has taken several steps to promote
AI. The UK Digital Strategy, published in March 2017,
recognizes AI as a key field that can help grow the United
Kingdom's digital economy.\21\ The UK's new budget, published
in November 2017, includes several provisions that have the
goal of establishing the UK as a world leader in AI, such as by
establishing a ``Centre for Data Ethics and Innovation'' to
promote the growth of AI, facilitating data access for AI
through ``data trusts,'' and funding 450 PhD researchers
working on AI.\22\
---------------------------------------------------------------------------
\21\ Department of Digital, Culture, Media, and Sport, UK Digital
Strategy, (United Kingdom: Department for Digital, Culture, Media, and
Sport, 2017), https://www.gov.uk/government/publications/uk-digital-
strategy.
\22\ Her Majesty's Treasury (HM Treasury), Autumn Budget 2017
(United Kingdom: HM Treasury, 2017), https://www.gov.uk/government/
publications/autumn-budget-2017-documents/autumn-budget-2017.
While the U.S. Government has put significant funding behind AI
R&D--approximately $1.1 billion in 2015--it has not done enough to
maintain U.S. leadership.\23\ The most ambitious AI program comes from
China, which as of 2014 surpassed the United States in terms of total
number of papers published and cited in AI fields, such as deep
learning.\24\ For both economic and national security reasons, the
United States cannot afford to cede its existing advantages in AI, and
should instead look to capitalize on its head start by developing a
strategy to support AI development and adoption. Such a strategy should
include policies to address the following:
---------------------------------------------------------------------------
\23\ Ibid.
\24\ ``National Artificial Intelligence Research and Development
Strategic Plan,'' (National Science and Technology Council, October
2016), https://www.nitrd.gov/PUBS/national_ai
_rd_strategic_plan.pdf.
Funding: The government should continue to expand its
funding to support the ``National Artificial Intelligence
Research and Development Strategic Plan,'' a set of R&D
priorities identified by the Networking and Information
Technology Research and Development (NITRD) program that
addresses strategic areas of AI in which industry is unlikely
to invest, as well as better plan and coordinate Federal
funding for AI R&D across different agencies.\25\
---------------------------------------------------------------------------
\25\ Ibid.
Skills: The Federal Government should support educational
efforts to ensure a strong pipeline of talent to create the
next generation of AI researchers and developers, including
through retraining and diversity programs, as well as pursue
immigration policies that allow U.S. businesses to recruit and
---------------------------------------------------------------------------
retain highly skilled computer scientists.
AI-Friendly Regulations: Federal and state regulators should
conduct regulatory reviews to identify regulatory barriers to
commercial use of AI in various industries, such as
transportation, health care, education, and finance.
Data Sharing: Some advances in AI are made possible when
large volumes of accurate and representative data are made part
of a data commons. The government should continue to supply
high-value datasets that enable advances in AI, such as its
efforts to produce standardize reference datasets for text
analysis and facial recognition. Similarly, Federal agencies
should facilitate data sharing between industry stakeholders,
such as the Department of Transportation's draft ``Guiding
Principles on Data Exchanges to Accelerate Safe Deployment of
Automated Vehicles.'' \26\
---------------------------------------------------------------------------
\26\ ``Draft U.S. DOT Guiding Principles on Voluntary Data
Exchanges to Accelerate Safe Deployment of Automated Vehicles,'' (U.S.
Department of Transportation, December 1, 2017) https://
www.transportation.gov/av/data.
Economic Indicators: Understanding the degree to which U.S.
firms have automated processes using AI will be a key metric to
assessing the effectiveness of various policies. The Census
Bureau should assess what type of economic data it should
gather from businesses to monitor and evaluate AI adoption,
much like it has tracked rural electrification or broadband
connectivity as key economic indicators.
How Should Policymakers Address Concerns About Workforce Disruption?
One of the most common fears about AI is that it will lead to
significant disruptions in the workforce.\27\ This fear is not new--
concerns about technology-driven automation have been a perennial
policy concern since at least the 1930s when Congress debated
legislation that would direct the Secretary of Labor to make a list of
all labor-saving devices and estimate how many people could be employed
if these devices were eliminated.\28\ This concern has been exacerbated
by a frequently-cited study by two Oxford academics which predicted
that 47 percent of U.S. jobs could be eliminated over the next 20
years.\29\
---------------------------------------------------------------------------
\27\ For a thorough rebuttal of this concern, see Robert D.
Atkinson, ``'It's Going to Kill Us!' And Other Myths of Artificial
Intelligence,'' (Information Technology and Innovation Foundation, June
2016), http://www2.itif.org/2016-myths-machine-learning.pdf.
\28\ John Scoville, ``Technology and the Volume of Employment,''
Proceedings of the Academy of Political Science 18, no. 1 (May 1938):
84-99.
\29\ Carl B. Frey and Michael A. Osbourne, ``The Future of
Employment: How Susceptible Are Jobs to Computerisation?'' (Oxford
Martin School, University of Oxford, Oxford, September 17, 2013),
http://www.oxfordmartin.ox.ac.uk/downloads/academic/
The_Future_of_Employment.
pdf.
---------------------------------------------------------------------------
This study's predictions are misleading and unlikely for at least
three reasons. First, the estimate includes a number of occupations
that have little chance of automation, such as fashion models and
barbers. Second, while this rate of productivity seems high and even
threatening, it is only slightly higher than rates enjoyed in the mid-
1990s when U.S. job creation was robust and unemployment rates low.
Third, it succumbs to what economists call the ``lump of labor''
fallacy which holds that once a job is gone, there are no other jobs to
replace it. The reality is that AI-driven productivity enables
organizations to either raise wages or reduce prices. These changes
lead to increases in spending, which in turn creates more jobs. And
given that consumers' wants are far from satisfied, there is no reason
to believe that this dynamic will change anytime soon.
But while predictions about massive AI-driven unemployment are
vastly overstated--indeed, by historical standards occupational churn,
the rate at which some jobs expand while others contract, is at its
lowest levels in 165 years--there will still be some worker
displacement as AI creates higher levels of productivity.\30\ So
policymakers can and should do more to help workers make transitions
between jobs and occupations, such as by providing strong social safety
net programs, reforming unemployment insurance, and offering worker
retraining. The failure to give workers training and assistance to move
into new jobs or occupations not only contributes to higher structural
unemployment, but also increases resistance to innovation and
automation.\31\
---------------------------------------------------------------------------
\30\ Robert D. Atkinson and John Wu, ``False Alarmism:
Technological Disruption and the U.S. Labor Market, 1850-2015,''
(Information Technology and Innovation Foundation, May 2017), http://
www2.itif.org/2017-false-alarmism-technological-disruption.pdf.
\31\ See forthcoming report: ``Technological Innovation,
Employment, and Workforce Adjustment Policies,'' (Information
Technology and Innovation Foundation, January 2018).
---------------------------------------------------------------------------
How Should Policymakers Provide Oversight of AI?
When it comes to AI, the primary goal of the United States should
be to accelerate the development and adoption of the technology. But as
with any technology, there will be some risks and challenges that
require government oversight. The presence of risk, however, does not
mean that the United States should embrace the precautionary principle,
which holds that new technology must first be proven safe before it can
be used. Instead, policymakers should rely on the innovation principle,
which says that policymakers should address risks as they arise, or
allow market forces to address them, and not hold back progress because
of speculative concerns. The innovation principle is especially useful
when fears about a new technology exceed public awareness and
understanding about how the technology works and how potential problems
will be mitigated.\32\
---------------------------------------------------------------------------
\32\ Daniel Castro and Alan McQuinn, ``The Privacy Panic Cycle: A
Guide to Public Fears About New Technologies,'' (Information Technology
and Innovation Foundation, September 2015), http://www2.itif.org/2015-
privacy-panic.pdf.
---------------------------------------------------------------------------
To understand why this is important, consider the differences
between the United States and the European Union in the Internet
economy. Compared to Europe, the United States has had more success in
the Internet economy, at least in part, because of its vastly more
simplified data protection regulations. Yet even as the United States
continues to produce the majority of the major global Internet
companies, the European Union has decided to double down on its onerous
data protection rules in the forthcoming General Data Protection
Regulation (GDPR), a far-reaching set of policies that will
substantially raise the costs, and in some cases, limit the feasibility
of using AI in Europe. For example, the GDPR creates both a right to
explanation and a right to human review for automated decisions, two
requirements that will make it difficult for companies to construct
business models that rely extensively on complex algorithms to automate
consumer-facing decisions. The GDPR also requires organizations to only
use data for the purposes for which they originally collected it, a
rule that strictly limits the application of AI to existing data.\33\
If the United States wants to compete for global leadership in AI, it
should be careful not to follow Europe down this path.
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\33\ Nick Wallace, ``UK Regulations Need an Update to Make Way for
Medical AI,'' Center for Data Innovation, August 12, 2017, http://
datainnovation.org/2017/08/uk-regulations-need-an-update-to-make-way-
for-medical-ai/.
---------------------------------------------------------------------------
While the United States should not replicate the European model, it
should create its own innovation-friendly approach to providing
oversight of the emerging algorithmic economy just as it has for the
Internet economy. Such an approach should prioritize sector-specific
policies over comprehensive regulations, outcomes over transparency,
and enforcement actions against firms that cause tangible harm over
those that merely make missteps without injury. For example, rather
than industry-wide rules requiring ``algorithmic transparency'' or ``AI
ethics''--proposals that focus on means, rather than ends--policymakers
should look to address specific problems, such as ensuring financial
regulators have the skills necessary to provide oversight of fintech
companies relying heavily on AI to make lending decisions or provide
automated financial advisors.
In many cases, regulators will not need to intervene because the
private sector will address problems about AI, such as bias or
discrimination, on its own--even if to outsiders an algorithm appears
to be a ``black box.'' After all, one company's hidden biases are
another company's business opportunities. For example, if certain
lenders were to use algorithms that consistently denied loans to ethnic
or religious minorities who have good credit, then their competitors
would have an incentive to target these individuals to gain new
customers.
Moreover, the private sector is actively seeking out solutions to
eliminate problems like unintentional bias in AI that may skew its
results.\34\ For example, a group of leading AI companies in the United
States have formed an association to develop and share best practices
to ensure that AI is fair, safe, and reliable, while another technology
trade association has publicly committed itself to ensuring that the
private sector designs and uses AI responsibly.\35\ Indeed, given that
U.S. companies are at the forefront of efforts to build AI that is safe
and ethical, maintaining U.S. leadership in this field will be
important to ensure these values remain embedded in the technology.
---------------------------------------------------------------------------
\34\ Cliff Kuang, ``Can A.I. Be Taught to Explain Itself?'' New
York Times, November 21, 2017, https://www.nytimes.com/2017/11/21/
magazine/can-ai-be-taught-to-explain-itself.html.
\35\ See ``Partnership on AI,'' https://www.partnershiponai.org/and
``AI Policy Principles,'' Information Technology Industry Council,
https://www.itic.org/resources/AI-Policy-Principles-FullReport2.pdf.
---------------------------------------------------------------------------
But policymakers should be careful not to misclassify certain
concerns as ``AI problems'' that would be best dealt with on a
technology-neutral basis. For example, discrimination in areas such as
access to financial services and housing are best addressed through
existing legal mechanisms. No new laws and regulations are needed
simply because a company uses AI, instead of human workers, to make
certain decisions.\36\ Companies cannot use AI to circumvent laws
outlawing discrimination.
---------------------------------------------------------------------------
\36\ Travis Korte and Daniel Castro, ``Disparate Impact Analysis is
Key to Ensuring Fairness in the Age of the Algorithm,'' Center for Data
Innovation, January 20, 2015, http://datainnovation.org/2015/01/
disparate-impact-analysis-is-key-to-ensuring-fairness-in-the-age-of-
the-algorithm/.
---------------------------------------------------------------------------
Finally, certain problems, such as sexism in hiring practices, are
not necessarily made worse by AI. On the contrary, using AI can
actually reduce human biases. For example, companies can use AI to
police undesirable behaviors, like automatically flagging job
advertisements that use gender-specific terminology, such as
``waitress'' instead of ``wait staff,'' or stereotypical images, such
as a female nurse.\37\ And unlike human processes, where it may take
years or decades to change social norms and company culture, businesses
can refine and tweak code over a period of days or weeks. For example,
Google changes its search engine 500 to 600 times per year.\38\ Thus
companies will likely have more success eliminating bias when it
appears in AI, than when it appears elsewhere in society.
---------------------------------------------------------------------------
\37\ Amber Laxton, ``Critics of `Sexist Algorithms' Mistake
Symptoms for Illness,'' Center for Data Innovation, August 3, 2015,
http://datainnovation.org/2015/08/critics-of-sexist-algorithms-mistake-
symptoms-for-illness/.
\38\ Daniel Castro, ``Data Detractors Are Wrong: The Rise of
Algorithms Is a Cause for Hope and Optimism,'' Center for Data
Innovation, October 25, 2016, http://datainnovation.org/2016/10/data-
detractors-are-wrong-the-rise-of-algorithms-is-a-cause-for-hope-and-
optimism/.
---------------------------------------------------------------------------
Conclusion
AI is a transformational technology that has the potential to
significantly increase efficiency and innovation across the U.S.
economy, creating higher living standards and improved quality of life.
But while the United States has an early advantage in AI given its top
talent in computer science and deep bench of companies, large and
small, investing in the field, many other countries are actively vying
to challenge U.S. leadership in this domain. In particular, China, with
its highly skilled computer science workforce and significant funding
for AI R&D, could easily catch and surpass the United States, leading
to it gaining economic and military advantages.
Unfortunately, U.S. policy debates about AI too often overemphasize
the potential impact on worker displacement from automation or bias
from algorithms and ignore the much more pressing concern about the
potential loss of competitiveness and defense superiority if the United
States falls behind in developing and adopting this key technology.
Yet, when it comes to AI, successfully integrating this technology
into U.S. industries should be the primary goal of policymakers, and
given the rapid pace at which other countries are pursuing this goal,
the United States cannot afford to rest on its laurels. To date, the
U.S. Government has not declared its intent to remain globally dominant
in this field, nor has it begun the even harder task of developing a
strategy to achieve that vision. Some may think this is unnecessary,
believing that the United States will automatically prevail in this
technology simply because it has a unique culture of innovation and has
prevailed on past technologies.\39\ Such views are naive and dangerous,
and if followed, likely will lead to the United States being surpassed
as the global leader in AI with significant negative consequences for
the U.S. economy and society. However, it is not too late to begin to
ensure continued U.S. leadership, and I commend you for holding this
hearing so that we can have this conversation.
---------------------------------------------------------------------------
\39\ Patrick Tucker, ``What the CIA's Tech Director Wants from
AI,'' Defense One, September 6, 2017, http://www.defenseone.com/
technology/2017/09/cia-technology-director-artificial-intelligence/
140801/.
Senator Wicker. Thank you, Mr. Castro.
Ms. Espinel.
STATEMENT OF VICTORIA ESPINEL, PRESIDENT AND CEO, BSA
THE SOFTWARE ALLIANCE
Ms. Espinel. Good morning, Chairman Wicker, Ranking Member
Schatz, and members of the Subcommittee. My name is Victoria
Espinel, and I am the President and CEO of BSA The
Software Alliance.
BSA is the advocate for the global software industry in the
United States and around the world. Our members are at the
forefront of developing artificial intelligence and related
software services. I commend the Subcommittee for a hearing on
this important topic, and I thank you for the opportunity to
testify.
At the outset, I think it's important to answer a key
question: What is AI? So let me provide you with a brief
anecdote. A 60-year-old woman was initially diagnosed with a
conventional form of leukemia. She went through chemotherapy to
treat the disease, but her recovery was unusually slow.
Conventional tests failed to reveal a problem, but her doctor
suspected that something was still wrong. After several
frustrating months, they turned to an AI-powered, cloud-based
system capable of cross-referencing the patient's genetic data
with insights gleaned from tens of millions of studies from
around the world. Within minutes, the doctors learned that the
patient might be suffering from an extremely rare form of
leukemia that required a unique course of treatment. The
doctors were able to quickly update her treatment plan and
watch her condition improve significantly.
This is AI: it's innovative; it's powerful; it's
lifesaving. AI is not the image that we see in science fiction
movies of robots demolishing tall buildings; instead, the AI
provided by BSA members today is a tool that uses data to help
people solve complex problems, simplify our daily lives,
improve business operations, and enhance government services.
AI is powered by software, which is itself a major engine
of economic growth. The software industry contributed more than
$1.14 trillion to the U.S. GDP in 2016--a $70 billion increase
in just 2 years. The software industry is a powerful job
creator supporting over 10.5 million jobs with a significant
positive impact on jobs and economic growth in every one of the
50 states.
For example, in Mississippi, software is contributing over
$800 million to its GDP and over 7,000 jobs, a 25 percent
increase in jobs in just 2 years. Over 4,000 miles away in
Hawaii, software is contributing over $1 billion to its GDP and
over 16,000 jobs. Across every single state in the country, the
economic impact of software is on the rise.
AI is helping all industry sectors. Whether it is securing
networks, improving health, or helping American farmers save
money, the impact of AI is already visible in every industry,
in every state, and across the globe.
We should also be prepared to address important issues that
may arise as AI-enabled services are used. Let me focus on two.
First, AI will change the skill sets needed for certain jobs,
and while new AI-related jobs will be created, there will be
shifts in the economy. BSA members are already helping
launching groundbreaking initiatives to provide free training,
including to youth and military veterans, to ensure that both
the current workforce and the next generation are prepared for
the future. We are dedicated to this work, and we look forward
to collaborating with all of you on this effort.
Second, we are mindful of the need to ensure that AI is
both trained and used fairly and responsibly. At the same time,
we recognize the potential of AI to make human decisions more
accurate and less biased and the need to push toward that
outcome.
As our companies seek to ensure responsible AI deployment,
there are several steps that Congress and the administration
can take. First, as I highlighted earlier, AI depends on data,
so we urge Congress to pass the Open Government Data Act, which
would make non-sensitive government data more open, more
available, and more usable for the general public.
Ranking Member Schatz, thank you for your great work as
sponsor of the Open Government Data Act. We hope that Congress
will act soon to send it to the President's desk. We also
encourage Congress and the administration to be leaders on
digital trade to encourage global data flows.
Second, we encourage increased investment in government
research, including on how AI can contribute to both positive
economic and social outcomes and policies that incentivize
private sector research and development.
And, third, we need to prioritize education and workforce
development so that our young people and our current workforce
are prepared for the future.
As part of all of this, we need to have a meaningful
dialogue with all stakeholders about how to address any
challenges that lie ahead. The legislation introduced by
Senators Cantwell, Young, and Markey is a good step, and we
thank you for that. Thanks to Senator Schatz as well for the
legislation that you are currently working on.
In closing, we look forward to working with all of you
towards a clear understanding of AI and to address the
challenges and embrace the opportunities ahead. BSA members are
part of the solution to these challenges, and we are eager to
work with you as we chart a responsible path forward.
Thank you, and I look forward to your questions.
[The prepared statement of Ms. Espinel follows:]
Prepared Statement of Victoria Espinel, President and CEO,
BSA--The Software Alliance
Good morning Chairman Wicker, Ranking Member Schatz, and members of
the Subcommittee. My name is Victoria Espinel, and I am the President
and CEO of BSA The Software Alliance.
BSA is the leading advocate for the global software industry in the
United States and around the world.\1\ Our members are at the forefront
of developing cutting-edge artificial intelligence (AI) and related
software-enabled technologies and services that are having a
significant impact on the U.S. and global economy. I commend the
Subcommittee for holding a hearing on this important topic, and I thank
you for the opportunity to testify on behalf of BSA.
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\1\ BSA's members include: Adobe, ANSYS, Apple, Autodesk, Bentley
Systems, CA Technologies, CNC/Mastercam, DataStax, DocuSign, IBM,
Microsoft, Oracle, salesforce.com, SAS Institute, Siemens PLM Software,
Splunk, Symantec, Trimble Solutions Corporation, The MathWorks, Trend
Micro and Workday.
---------------------------------------------------------------------------
I. AI: Defining the Landscape
The term ``artificial intelligence'' often conjures images of all-
knowing robots with physical and cognitive abilities far superior to
those of their human creators. The actual AI services that are in the
market today--and that BSA members provide--bear no resemblance to the
sinister images of the future that consumers often see in the movies,
with robots taking over big cities and small towns.
Instead, they increasingly are becoming a foundational technology
that drives many products and services that people use every day.
Whether it is a personal digital assistant that helps consumers locate
the nearest restaurant, a fraud detection monitoring service that
prevents criminals from placing charges on credit cards, or a tool that
helps teachers identify students with additional needs and develop
personalized lesson plans, we increasingly rely on a diverse range of
AI-enabled services every day.
But what is ``AI''?
Although definitions of AI vary, one common description of AI is
that it refers to machines that act intelligently in pursuit of human-
defined objectives. At its core, AI is simply a tool. It includes a
broad range of technologies, but the AI systems that BSA members
largely provide assist in the analysis of enormous volumes of data to
find connections that improve the quality and accuracy of human
decision-making. Although some AI systems have a limited degree of
autonomy, such as submarines that map the ocean bed and measure ocean
currents, and others are minutely supervised, such as robot surgical
tools assisting doctors with hip replacement surgeries, the vast
majority provide advice and recommendations to humans rather than
acting independently. AI makes possible important tasks that would
otherwise be economically or physically infeasible, such as inspecting
wind turbine blades or the interior of oil pipelines.
AI systems, like other software systems, use sophisticated
algorithms. An algorithm is a set of instructions that processes
various inputs and provides an output in a systematized way. The
algorithms used in AI are particularly well-suited to analyzing massive
volumes of data from many different sources, and in identifying
patterns across the enormous number of variables in such data that may
interact in complex and unexpected ways. Through this analysis, AI
systems can enhance perception, learning, reasoning, and decision-
making, and improve the ability of people to solve complex and
challenging problems.
The use of systems, including software, to help people solve
complex problems is not new. Research into AI dates back many decades,
but we have witnessed tremendous advances in AI capabilities over the
past five to ten years. These advances have been fueled by a number of
related developments, including the proliferation of technologies that
generate vast amounts of data, the affordability of data storage, and
ever-growing data processing capabilities.
BSA members have made significant investments in enhancing these
data-driven technologies to develop innovative AI solutions for use
across a broad range of applications in a wide variety of contexts.
II. AI Services Provide Substantial Benefits
Advances in AI and software-enabled data analytics are fueling job
and economic growth in the United States and around the world,
improving how businesses in every sector operate, and producing real
societal gains. We must recognize that AI will change the skill sets
needed for certain jobs. And while new, AI-related jobs will be
created, there will be shifts in the labor market. And although we
should be mindful of the need to ensure that AI is deployed fairly and
responsibly, we should also recognize the potential of AI to make human
decisions more accurate and less biased, and thereby to promote
fairness and inclusiveness across all segments of society.
A. AI and Related Software Services Are Creating Jobs and Economic
Growth
In high-tech and low-tech industries alike, the analysis of data
has made businesses more agile, responsive, and competitive, boosting
the underlying productivity of many key pillars of our economy.
The economic implications of the data revolution--and AI and
related software solutions that leverage that data--are enormous.
Economists predict that making better use of data could lead to a
``data dividend'' of $1.6 trillion in the next four years, and that
data-enabled efficiency gains could add almost $15 trillion to global
GDP by 2030.\2\ In addition, experts predict that applications of AI
technologies could grow the global economy by $7.1 to $13.17 trillion
over the next eight years.\3\
---------------------------------------------------------------------------
\2\ See BSA, What's the Big Deal With Data? 14 (Oct. 2015),
available at http://data.bsa.org/wp-content/uploads/2015/12/
bsadatastudy_en.pdf. The potential of digital data to improve the
healthcare system is substantial: some estimates predict that if the
healthcare sector were to use data more effectively to drive efficiency
and quality, the sector could save more than $300 billion every year.
See James Manyika et al., Big Data: The Next Frontier for Innovation,
Competition, and Productivity, McKinsey Global Institute (May 2011),
available at http://www.mckinsey.com/insights/business_technology/
big_data_the_next_frontier_for_innovation.
\3\ See Disruptive technologies: Advances that will transform life,
business, and the global economy, McKinsey Global Institute (May 2013),
available at http://www.mckinsey.com/business-functions/digital-
mckinsey/our-insights/disruptive-technologies.
---------------------------------------------------------------------------
AI systems are powered by software, which itself is a major engine
of economic growth. In September, Software.org: the BSA Foundation
released a study with data from the Economist Intelligence Unit (EIU)
showing that the software industry alone contributed more than $1.14
trillion to U.S. GDP in 2016--a $70 billion increase in just two
years.\4\ The study also showed that the software industry is a
powerful job creator, supporting over 10.5 million jobs, with a
significant impact on job and economic growth in each of the 50
states.\5\
---------------------------------------------------------------------------
\4\ Software.org: The BSA Foundation, The Growing $1 Trillion
Economic Impact of Software 5 (Sept. 2017), available at https://
software.org/wp-content/uploads/2017_Software_Economic
_Impact_Report.pdf.
\5\ Id.
---------------------------------------------------------------------------
B. AI and Related Software Services Are Improving Every Industry
The benefits of AI are not limited to the software sector. In fact,
AI innovation is stimulating growth across all industry sectors as
businesses, big and small, use AI and related software services to
improve supply chains, secure their networks, and evaluate how to
improve their products and services. There are numerous examples of
this positive impact across a wide swath of industries, for instance:
Cybersecurity. AI tools are revolutionizing how we monitor
network security, helping analysts parse through hundreds of
thousands of security incidents per day to weed out false
positives and identify threats that warrant further attention
by network administrators. By automating responses to routine
incidents and enabling security professionals to focus on truly
significant threats, AI-enabled cyber tools are helping
enterprises stay ahead of their malicious adversaries.\6\
---------------------------------------------------------------------------
\6\ For example, IBM's Watson for Cyber Security is a cybersecurity
tool that can analyze 15,000 security documents per day--a rate
essentially impossible for any individual to achieve. Watson's data
processing capabilities enable analysts to more quickly identify
incidents that require human attention. See IBM, IBM Delivers Watson
for Cyber Security to Power Cognitive Security Operations Centers (Feb.
13, 2017), https://www-03.ibm.com/press/us/en/press
release/51577.wss; Jason Corbin, Bringing the Power of Watson and
Cognitive Computing to the Security Operations Center, Security
Intelligence (Feb. 13, 2017), https://securityintelligence
.com/bringing-the-power-of-watson-and-cognitive-into-the-security-
operations-center/?cm_mc_uid
=70595459933115020631816&cm_mc_sid_50200000=1503364089&cm_mc_sid_5264000
0=150336
5578. Splunk uses a similar model, with machine-learning algorithms
conducting real-time analysis and processing of massive volumes of data
from all sensors on a network to identify anomalies, feeding
visualization tools that help network administrators efficiently triage
security incidents. See David Braue, Machine learning key to building a
proactive security response: Splunk, CSO Online (Aug. 20, 2015),
https://www.cso.com.au/article/582483/machine-learning-key-building-
proactive-security-response-splunk/. Microsoft's Windows 10 Anniversary
Edition introduced AI-driven capabilities for automatically isolating
suspicious network traffic pending adjudication by network
administrators. See Chris Hallum, Defense Windows clients from modern
threats and attacks with Windows 10, Channel 9 video content (Oct. 6,
2016), available at https://channel9.msdn.com/events/Ignite/2016/
BRK2135-TS); ``Intelligent Security: Using Machine Learning to Help
Detect Advanced Cyber Attacks,'' https://www.microsoft.com/en-us/
security/intelligence.
Financial Services. AI is improving fraud detection by
providing companies with real-time information that helps them
identify and investigate different types of fraud, reducing the
losses attributed to fraudsters by billions of dollars. In a
matter of seconds, machine learning algorithms can generate a
risk score for a transaction by parsing through large volumes
of data about the vendor and the purchaser to determine the
likelihood of fraud.\7\ These tools are protecting consumers
from the risk of fraudulent charges and from the frustration
associated with ``false declines.''
---------------------------------------------------------------------------
\7\ See generally Pablo Hernandez, CA Technologies Uses AI Tech to
Combat Online Fraud, eSecurityPlanet, May 4, 2017, available at https:/
/www.esecurityplanet.com/network-security/ca-technologies-uses-ai-tech-
to-combat-online-fraud.html.
Agriculture. AI is helping farmers tackle some of the
biggest issues they face, including declining crop yields and
changing weather patterns, through precision farming, better
data analysis, and improved operational efficiency. For
instance, tools like computer vision and deep-learning
algorithms are enabling farmers more effectively to process
data for purposes of monitoring crop and soil health.\8\
---------------------------------------------------------------------------
\8\ See Kumba Sennaar, AI in Agriculture--Present Applications and
Impact, techemergence (Nov. 17, 2017), https://www.techemergence.com/
ai-agriculture-present-applications-impact/.
Manufacturing. AI-enabled tools are also helping factory
owners streamline their manufacturing processes and resolve
problems common to most factories, such as inaccurate demand
forecasting and capacity planning, unexpected equipment
failures and downtimes, and supply chain bottlenecks.
Predictive maintenance, for instance, allows manufacturers to
achieve 60 percent or more reduction in unscheduled system
downtime. Cameras powered by computer vision algorithms can fix
product defects immediately and identify root causes of
failure. AI thus enables manufacturers to reduce waste, shorten
production periods, increase yields on production inputs, and
improve both revenue and workplace safety.\9\
---------------------------------------------------------------------------
\9\ See Mariya Yao, Factories Of The Future Need AI To Survive And
Compete, Forbes.com (Aug. 8, 2017), https://www.forbes.com/sites/
mariyayao/2017/08/08/industrial-ai-factories-of-future/#2d7ab2fd128e.
Healthcare. AI technologies are already providing solutions
that help save lives. A 2016 Frost & Sullivan report predicts
that AI has the potential to improve health outcomes by 30 to
40 percent.\10\ AI is helping fuel these improved health
outcomes not by replacing the decision-making of healthcare
professionals, but by giving these professionals new insights
and new ways of analyzing and understanding the health data to
which they have access. For example, AI tools are powering
machine-assisted diagnosis and surgical applications are being
used to improve treatment options and outcomes. Image
recognition algorithms are helping pathologists more
effectively interpret patient data, thereby helping physicians
form a better picture of patients' prognosis.\11\ The ability
of AI to process and find patterns in vast amounts of data from
disparate sources is also driving important progress in
biomedical and epidemiological research.\12\
---------------------------------------------------------------------------
\10\ See From $600 M to $6 Billion, Artificial Intelligence Systems
Poised for Dramatic Market Expansion in Healthcare, Frost & Sullivan
(Jan. 5, 2016), https://ww2.frost.com/news/press-releases/600-m-6-
billion-artificial-intelligence-systems-poised-dramatic-market-
expansion-healthcare.
\11\ See e.g., Meg Tirrell, From coding to cancer: How AI is
changing medicine, cnbc.com (May 11, 2017), https://www.cnbc.com/2017/
05/11/from-coding-to-cancer-how-ai-is-changing-medicine.html.
\12\ For instance, AI is helping biologists who are aiming to treat
100 molecular genetic diseases by 2025. See Splunk, Machine Learning
Helps Recursion Pharmaceuticals Treat Genetic Diseases (Nov. 7, 2017),
https://www.splunk.com/en_us/newsroom/press-releases/2017/splunk-
machine-learning-helps-recursion-pharmaceuticals-treat-genetic-
diseases.html. In another example, Microsoft researchers are also using
AI and related technologies to better understand the behavior of cells
and their interaction, which could ultimately help ``debug'' an
individual's specific form of cancer and allow doctors to provide
personalized cancer treatment. See generally, Microsoft, Biological
Computation, https://www.microsoft.com/en-us/research/group/biological-
computation/.
Education. AI technologies offer tools for students,
teachers, and administrators to help students learn more
effectively both within and outside of the classroom. AI
programs can, for example, analyze a student's performance in a
particular skill across subjects over the course of a year and
automatically provide new content or specified learning
parameters, offering students continual, individualized
practice and feedback. They can also help teachers better
understand student performance, quickly identify students that
need particular attention, and develop lesson plans that
customize instruction, content, pace, and testing to individual
students' strengths and interests.\13\ AI solutions also are
helping administrators track attendance patterns and gain
insights on student performance more broadly.\14\
---------------------------------------------------------------------------
\13\ See Software.org: The BSA Foundation, The Growing $1 Trillion
Economic Impact of Software, supra note 4, at 7; see also Daniel
Faggella, Examples of Artificial Intelligence in Education,
TechEmergence (Mar. 7, 2017), https://www.techemergence.com/examples-
of-artificial-intelligence-in-education/.
\14\ Benjamin Herold, Are schools ready for the power and problems
of big data?, Education Week (Jan. 11, 2016), available at http://
www.edweek.org/ew/articles/2016/01/13/the-future-of-big-data-and-
analytics.html.
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C. AI Services Provide Tremendous Societal Benefits
The range of potential societal benefits from the use of AI
services is equally vast. For example, AI solutions are at the heart of
new devices and applications that improve the lives of people with
disabilities, including helping people with vision-related impairments
interpret and understand photos and other visual content.\15\ This
technology opens new possibilities for people with vision impairments
to navigate their physical surroundings, giving them increased
independence and greater ability to engage with their communities.
---------------------------------------------------------------------------
\15\ For instance, Microsoft recently released an intelligent
camera app that uses a smartphone's built-in camera functionality to
describe to low-vision individuals the objects that are around them.
See Microsoft, Seeing AI, https://www.microsoft.com/en-us/seeing-ai/.
---------------------------------------------------------------------------
AI is also helping governments improve constituent services in ways
that save time, money, and lives. For example, cities are optimizing
medical emergency response processes using AI-based systems, enabling
them to more strategically position personnel and reduce both response
times and the overall number of emergency trips.\16\ AI is also helping
to leverage data to improve disaster response and relief efforts,
including after the 2015 earthquake in Nepal.\17\
---------------------------------------------------------------------------
\16\ See Kevin C. Desouza, Rashmi Krishnamurthy, and Gregory S.
Dawson, Learning from public sector experimentation with artificial
intelligence, Brookings Institution (June 23, 2017), https://
www.brookings.edu/blog/techtank/2017/06/23/learning-from-public-sector-
experimentation-with-artificial-intelligence/.
\17\ See Patrick Meier, Virtual Aid to Nepal: Using Artificial
Intelligence in Disaster Relief, Foreign Affairs (June 1, 2015),
available at https://www.foreignaffairs.com/articles/nepal/2015-06-01/
virtual-aid-nepal.
---------------------------------------------------------------------------
* * * * *
Whether it is detecting financial fraud, improving health outcomes,
making American farmers more competitive, or enhancing government and
emergency services, the impact of AI and related software services is
already visible in every industry, in every state, and across the
globe.
III. Fostering Consumer Trust in AI
Even as society gains from the substantial benefits that AI offers,
we also recognize that there may be legitimate concerns about how AI
systems are deployed in practice, which may also affect trust and
confidence in AI. In particular, as people increasingly apply AI
services in new contexts, questions may arise about how they operate,
whether they treat people fairly and are free from improper bias, and
their impact on jobs. Like many technologies, AI has an almost infinite
range of beneficial uses, but we should also take appropriate steps to
ensure that it is deployed responsibly. We recognize that responsible
deployment of AI should instill consumer confidence that these
important issues will be appropriately addressed.
A. Enhancing Understanding of AI Systems
Building trust and confidence in AI-enabled systems is an important
priority. In some instances, the complexity of these technologies,
which are designed to identify patterns and connections that humans
could not easily identify on their own, can make it challenging to
explain how certain aspects of AI systems work. BSA members understand
that, in order to promote trust, companies that build and deploy AI
systems will need to provide meaningful information to enhance
understanding of how these systems operate.
Indeed, ensuring that AI systems operate as intended and treat
people fairly is an important priority. We are eager to participate in
meaningful dialogues with other stakeholders about how best to
accomplish that goal, and we welcome opportunities such as this one to
help advance that dialogue. Currently, relevant technical tools and
operational processes that could improve understanding and confidence
in AI systems are still being developed, and it is an area of robust
research. Although more work needs to be done, it is already clear that
expectations are highly context-specific--and demands will vary based
on this context. As we seek to address these important issues, we will
aim to ensure that we remain sufficiently flexible to respond to
concerns, and to adapt to the changing landscape as these emerging
technologies, and potential solutions to new challenges, continue to
evolve.
B. Preparing the Workforce for the Jobs of the Future
As AI services improve every industry, they will likely have a
multi-dimensional impact on employment. The deployment of AI in the
workplace will enable employees to focus on tasks that are best suited
to uniquely human skillsets, such as creativity, empathy, foresight,
judgment, and other social skills. Although there appears to be no
consensus on the precise impact AI will have on employment, there is
broad recognition that widespread deployment of these technologies will
create demand for new types of jobs, and that these jobs often will
require skills that many workers today do not yet have.
Current estimates indicate the United States will not have enough
workers to meet the predicted high demand for computer science-related
jobs. For example, by 2020, the U.S. Bureau of Labor Statistics
predicts that there will be 1.4 million computing jobs, but just
400,000 computer science students with the skills necessary to fill
those jobs.\18\ It is imperative that the United States takes steps now
to ensure that we have a sufficient pipeline of workers with the skills
needed to perform these new, high-quality jobs.
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\18\ See Allie Bidwell, Tech Companies Work to Combat Computer
Science Education Gap, U.S. News & world report, Dec. 27, 2013,
available at https://www.usnews.com/news/articles/2013/12/27/tech-
companies-work-to-combat-computer-science-education-gap.
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Yet even these estimates do not take into account the extent to
which the use of AI may require new skills. Because AI services will
likely be integrated across all sectors of the economy, the new jobs AI
creates, and the new skills that will be needed, will reach beyond the
tech sector, and will also likely extend to workers in both urban and
rural areas. Indeed, many of these jobs will ``look nothing like those
that exist today,'' and will include ``entire categories of new,
uniquely human jobs'' that will require ``skills and training that have
no precedents.'' \19\ As a result, one key challenge that lies ahead is
determining how to ensure that the U.S. workforce has the skills
necessary for the future.
---------------------------------------------------------------------------
\19\ H. James Wilson, Paul R. Daugherty, Nicola Morini-Bianzino,
The Jobs that Artificial Intelligence will Create, MIT Sloan Management
Review (Mar. 23, 2017), available at https://sloanreview.mit.edu/
article/will-ai-create-as-many-jobs-as-it-eliminates/.
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BSA members are working hard to help address this challenge. BSA
recognizes that this will require a multi-faceted solution, including
cooperation with public and private stakeholders. We seek to identify
opportunities and partnerships that focus on retraining the workforce
with new skills, creating a pipeline of workers with skills to fill the
next generation of jobs, increasing access to those jobs for skilled
workers, and increasing deployment of cloud services, which facilitate
employment and collaboration in different geographic regions.
Notably, BSA members already have begun helping workers and youth
acquire new skills that will enable them to leverage AI systems.\20\
BSA members offer several high-tech and business training programs,
including at the high school level. Some programs target populations
not traditionally associated with tech jobs, such as military
veterans.\21\ These initiatives illustrate just some of the ways in
which AI-based employment concerns can be meaningfully addressed.
---------------------------------------------------------------------------
\20\ See, e.g., Allen Blue, How LinkedIn is Helping Create Economic
Opportunity in Colorado and Phoenix (Mar. 17, 2016), https://
blog.linkedin.com/2016/03/17/how-linkedin-is-helping-create-economic-
opportunity-in-colorado-and-phoenix; Markel Foundation, Why Microsoft
and the Markle Foundation are Working Together to Connect Workers with
New Opportunities in the Digital Economy, https://www.markle.org/
microsoft. IBM, for instance, has established Pathways in Technology
Early College High Schools (P-TECH Schools). P-TECH schools are
innovative public schools that offer students the opportunity to earn a
no-cost associates degree within six years in fields such as applied
science and engineering--and to acquire the skills and knowledge
necessary to pursue further educational opportunities or to step easily
into well paying, high-potential informational technology jobs. IBM
designed the P-TECH model to be both widely replicable and sustainable
as part of an effort to reform career and technical education. See IBM,
IBM and P-TECH, https://www-03.ibm.com/press/us/en/presskit/42300.wss.
Likewise, Salesforce offers free high-tech and business skills training
through Trailhead, its online learning platform, with the goal of
preparing them for the estimated 3.3 million jobs created by the
Salesforce economy worldwide from 2016 to 2022, nearly 1 million of
which are forecasted to be in the United States. See International Data
Corporation, The Salesforce Economy Forecast: 3.3 Million New Jobs and
$859 Billion New Business Revenue to Be Created from 2016 to 2022 (Oct.
2017), available at http://www.salesforce.com/assets/pdf/misc/idc-
study-salesforce-economy.pdf; see also Gavin Mee, How the Salesforce
Economy is Driving Growth and Creating Jobs, Oct. 24, 2017, available
at https://www.salesforce.com/uk/blog/2017/10/idc-how-the-salesforce-
economy-is-driving-growth-and-creating-jo; Gavin Mee, Guest Blog: Gavin
Mee, Salesforce--Evolving tech means change in digital skills, TechUK
(Apr. 26, 2017), at https://www.techuk
.org/insights/opinions/item/10695-guest-blog-gavin-mee-salesforce-
evolving-tech-means-change
-in-digital-skills.
\21\ For example, the Splunk4Good initiative, which partners with
non-profits, is helping military veterans and their families, along
with youth, train for careers in technology, providing free access to
Splunk licenses and its extensive education resources to help them
attain marketable skillsets. See Splunk, Splunk Trains Workforce of
Tomorrow With Amazon Web Services, NPower, Wounded Warrior Project and
Year Up, (Sept. 26, 2017) https://www.splunk.com/en_us/newsroom/press-
releases/2017/splunk-trains-workforce-of-tomorrow-with-amazon-web-
services-npower-wounded-warrior-project-and-year-up.html.
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IV. Opportunities for Congress and the Administration to Facilitate AI
Innovation
As innovation in AI and related software services increasingly
fuels growth in the global economy, countries around the world are
taking steps to invest in education, research, and technological
development to become a hub for AI innovation. For example, the UK
government recently released an Industrial Strategy, which identifies
putting the UK at the forefront of the AI and data revolution as one of
four key strategies that will secure its economic future.\22\ In the
EU, the European Parliament recently issued a report on civil law rules
regarding robotics, which highlights the opportunities robotics and AI
offer and encourages investment in such technology so Europe can
maintain leadership in this space.\23\ Likewise, in Japan, the
government recently issued a new strategy designed to strengthen
collaboration between industry, the government, and academia on matters
related to robotics, and also issued a report offering the first
systematic review of AI networking issues in Japan.\24\ In China, the
government has issued a ``Next Generation Artificial Intelligence
Development Plan,'' which lays out objectives for AI development in
China for the next 13 years and calls on China to become a global AI
innovation center by 2030.\25\
---------------------------------------------------------------------------
\22\ See UK Secretary of State for Business, Energy and Industrial
Strategy, Industrial Strategy Building a Britain fit for the future
(Nov. 2017), available at https://www.gov.uk/government/uploads/system/
uploads/attachment_data/file/662541/industrial-strategy-white-paper-
print-version.pdf.
\23\ See European Parliament 2014-2019, Resolution of 16 February
2017 with recommendations to the Commission on Civil Law Rules on
Robotics, Eur. Parl. Doc. P8_TA (2017)0051, http://
www.europarl.europa.eu/sides/getDoc.do?pubRef=-//EP//NONSGML+TA+P8-TA-
2017-0051+0
+DOC+PDF+V0//EN.
\24\ See Fumio Shimpo, Japan's Role in Establishing Standards for
Artificial Intelligence Development, Carnegie Endowment for
International Peace (Jan 12, 2017), http://carnegie
endowment.org/2017/01/12/japan-s-role-in-establishing-standards-for-
artificial-intelligence-development-pub-68311.
\25\ See Elsa Kania, China's Artificial Intelligence Revolution,
The Diplomat (Jul. 27, 2017), available at https://thediplomat.com/
2017/07/chinas-artificial-intelligence-revolution/.
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In the United States, a flexible policy framework that facilitates
responsible AI deployment and increased investment will be key to
preserving U.S. global economic competitiveness. An essential part of
that effort will be ensuring the ability to access data, and to
transfer that data seamlessly across borders, which are vital for AI to
flourish. It also will be important to support investment in AI-related
education, workforce development, and research. To that end, there are
several steps that Congress and the Administration could take to spur
AI innovation and continued economic growth.
A. Pass OPEN Government Data Act
First, Congress should pass the OPEN Government Data Act. This
legislation, which the House recently passed as Title II of the
Foundations for Evidence-Based Policymaking Act, recognizes that
government-generated data is a national resource that can serve as a
powerful engine for creating new jobs and a catalyst for economic
growth. To that end, the OPEN Government Data Act would require
agencies to make non-sensitive government data more open, available,
and usable for the general public. Making such data more readily
available will improve government transparency, promote government
efficiency, and foster innovation of data-driven technologies such as
artificial intelligence.
We would like to thank Ranking Member Schatz for his tireless work
as an original sponsor of the OPEN Government Data Act. We are hopeful
that the Senate will act soon to secure its final passage into law.
B. Support Efforts to Promote Digital Trade and Facilitate Data Flows
We also urge Congress and the Administration to continue supporting
efforts to expand digital trade. Indeed, the new digital data economy,
which increasingly relies on AI and related software services, will
benefit from a globally recognized system for digital trade that
facilitates cross-border data flows and establishes clear rules,
rights, and protections. There are several opportunities for Congress
and the Administration to lead in this area.
First, the ongoing NAFTA discussions provide an important
opportunity to modernize the trade agreement, which was initially
negotiated when digital services were in their infancy. We are
encouraged that the Administration has made it an objective to seek to
prohibit market access barriers to digital trade, including
restrictions on data transfers, data localization mandates, and
technology transfer requirements.
Second, another key priority is ensuring that transatlantic trade
continues to thrive. In particular, we appreciate Congress's and the
Administration's leadership on issues relating to the EU-U.S. Privacy
Shield, which both protects privacy and facilitates data transfers
between the EU and United States. We encourage your continued support
as the Administration proceeds with its ongoing successful
implementation of the framework.
Third, as other countries seek to modernize their trade policies,
the Administration should engage key global partners to ensure that new
trade initiatives facilitate data-driven innovation and protect against
market access barriers for e-commerce and digital trade.
C. Invest in AI research, education, and workforce development
Unlocking the full promise of AI technologies also requires a long-
term strategy of investing in education, workforce development, and
research. Because human beings ultimately drive the success of AI,
supporting education, training, and research is essential to extracting
the maximum level of benefit that AI technologies offer.
As an initial matter, Congress and the Administration should ensure
that education programs are developing human talent more effectively.
Broadly speaking, this means that Congress and the Administration
should support science, technology, engineering, and mathematics (STEM)
education at all levels. It also means creating and supporting programs
that help educate researchers and engineers with expertise in AI, as
well as specialists who apply AI methods for specific applications and
users who operate those applications in specific settings.\26\ For
researchers and engineers, these programs should include training in
computer science, statistics, mathematical logic, and information
theory, and for specialists, they should focus on software engineering
and related applications.\27\
---------------------------------------------------------------------------
\26\ See U.S. Executive Office of the President, Preparing for the
Future of Artificial Intelligence, National Science and Technology
Council Committee on Technology 26 (Oct. 2016), available at https://
obamawhitehouse.archives.gov/sites/default/files/whitehouse_files/
microsites/ostp/NSTC/preparing_for_the_future_of_ai.pdf.
\27\ See id.
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Congress and the Administration should also support the development
of new and innovative ways to ensure the U.S. workforce is prepared for
the jobs of the future. Because AI will generate new jobs in categories
both known and unforeseen, we need to develop thoughtful and effective
approaches to equip the U.S. workforce with the skills necessary to
seize the opportunities these new technologies create and to optimize
the role of AI in modern life.
Continued scientific research is essential to fully tapping the
potential of AI technology. Congress and the Administration should
therefore also promote both public and private sector research to help
ensure that the United States remains a leader in this space. The U.S.
Government should invest in the types of ``long-term, high-risk
research initiatives'' in which the commercial sector may be reluctant
to invest. In the past, such R&D investments have led to
``revolutionary technological advances. . .[such as] the Internet, GPS,
smartphone speech recognition, heart monitors, solar panels, advanced
batteries, cancer therapies, and much, much more.'' \28\ Congress and
the Administration should also adopt policies that incentivize private-
sector R&D, including by expanding access to financing.
---------------------------------------------------------------------------
\28\ See The National Artificial Intelligence Research and
Development Strategic Plan (Oct. 2016), available at https://
www.nitrd.gov/PUBS/national_ai_rd_strategic_plan.pdf.
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Passing the OPEN Government Data Act, supporting efforts to promote
digital trade and facilitate cross-border data flows, and investing in
AI research, education, and workforce development will be critical to
maximizing the opportunities AI presents and helping to ensure that the
United States maintains leadership in AI innovation and deployment,
even as other nations increase their own efforts to take advantage of
the possibilities that AI offers.
* * *
We appreciate Congress's leadership on the important issue of
facilitating AI innovation and its responsible deployment. Thank you
and I look forward to your questions.
Senator Wicker. Thank you very much.
Dr. Gil.
STATEMENT OF DR. DARIO GIL, Ph.D., VICE PRESIDENT,
AI AND IBM Q
Dr. Gil. Chairman Wicker, Ranking Member Schatz, members of
the Subcommittee, thank you for inviting me here today. My name
is Dario Gil, and I am the Vice President of AI and quantum
computing at IBM.
The idea of creating a thinking machine is not new, and
precedes modern computing. Calculating machines were built in
antiquity and improved throughout history by many
mathematicians. The term ``artificial intelligence'' was first
introduced 61 years ago in 1956, and AI, as an academic
discipline, took off. Three years later, IBM scientist Arthur
Samuel coined the term ``machine learning'' to refer to
computer algorithms that learn from and make predictions on
data by building a model from sample inputs without following a
set of static instructions.
One type of machine learning and AI algorithm that has
gained tremendous attention over the past several years is an
artificial neural network, notably, deep learning. These
networks are inspired by the architecture of the human brain,
with neurons organized as layers, and different layers may
perform different kinds of operations on their inputs. When
presented with sample data, neural net can be trained to
perform a specific task, such as recognizing speech or images.
Over the last decade, the explosion of digital data and the
growth in processing speed and power have made it possible to
use neural nets in real-world solutions.
While many tend to focus on the automation features of AI,
we believe its true impact will be felt in humans carrying out
complex tasks which they cannot do on their own. My prepared
testimony provides detailed examples on the many ways in which
IBM's AI platform for enterprise business, Watson, is being
used to augment human abilities across many industries, from
strengthening cybersecurity to enhancing the customer
experience to improving agriculture and optimizing supply
chains. AI is playing a bigger and bigger role in all realms of
commerce, and its uses will only grow.
Now, there is no question the advent of AI will impact the
nature of jobs, yet history suggests that even in the face of
technological transformation, employment continues to growth
with economic expansion and the creation of entirely new jobs
despite the disappearance of some occupations.
Jobs are made out of tasks. Those tasks that cannot be
automated, that cannot be automated by AI, are those in which
workers will provide the greatest value, commanding higher
wages and incomes as a result.
Now, the creation of AI itself will require new job
categories associated with how we design and train them, how we
secure them, and verify that they work as planned, and how we
integrate them into our workflows. The application of AI will
change our professions, opening up new categories of work and
increasing demand for some existing professions as we combine
the capabilities of these AI systems with our own expertise.
For example, many more cybersecurity professionals will be
needed to engage with AI systems and act decisively upon the
threat to information they provide.
We must address the problem of shortage of workers with the
skills needed for these and many other roles. A useful example
in this regard is software programming, which is taught as a
critical skill in many high schools and colleges. We should
promote a similar movement for AI techniques, such as machine
learning.
To enjoy the full benefits of AI, we'll also need to have
confidence in the recommendations, judgments, and uses of AI
systems. In some cases, users will need to justify why an AI
system produced its recommendations. For example, doctors and
clinicians using AI systems to support medical decisionmaking
may require to provide specific explanations for a diagnosis or
course of treatment, both for regulatory and liability reasons.
IBM is actively innovating in this field. We're deriving
best practices for how, when, and where AI algorithms should be
used. We're creating new AI algorithms that can be trusted, are
explainable, and are more accurate.
Of course, no single company can guarantee the safe and
responsible use of such a pervasive technology. For this
reason, IBM is a founding member of the Partnership on AI, a
collaboration with other key industry leaders and many
scientific and nonprofit organizations. It's focused on the
formulation of best practices, on AI technologies, and
advancing the public's understanding of AI.
In addition, a recently created MIT-IBM Watson AI Lab has
one of its research pillars advancing shared prosperity with
AI, exploring how AI can deliver economic and societal benefits
to a broader range of people, nations, and enterprises.
In a similar way, we look forward to working closely with
the Members of Congress to ensure the responsible, ethical, and
equitable use of AI as this technology continues to evolve.
[The prepared statement of Dr. Gil follows:]
Prepared Statement of Dario Gil, Vice President, AI and IBM Q
Introduction
Chairman Wicker, Ranking Member Schatz, members of the
Subcommittee. Thank you for inviting me here today. My name is Dario
Gil and I am Vice President, AI and quantum computing at IBM.
We have arrived at a remarkable moment in the history of
information technology. An explosion of data and computation has given
us access to massive amounts of digitized knowledge. With it, we have
enormous intelligence and power to see patterns and solve problems we
never could have before.
Increasingly, the engine we use to tap into this knowledge is
artificial intelligence. We can now train algorithms with emerging
Artificial Intelligence (AI) technologies to learn directly from data
by example. Moreover, we can do this at scale cost through cloud
networks to create machines that help humans think. For these reasons,
AI is the most important technology in the world today.
The rise of AI has been accompanied by both boundless enthusiasm
about its ability to transform our lives, and fears it could
potentially harm or displace us. At IBM, we take a different approach.
We are guided by the use of artificial intelligence to augment human
intelligence. We focus on building practical AI applications that
assist people with well-defined tasks. We believe people working
collaboratively with these learning systems is the future of expertise.
In my testimony, I'll provide an overview of AI and describe how
work in this field has evolved. Then I'll offer some examples of the
rapidly growing commercial applications of IBM Watson, the best-known
artificial intelligence platform for enterprise business today. I'll
also look at how we're beginning to combine AI with other emerging
technologies, such as blockchain, to optimize business and provide
trust in transactions. I'll describe how AI will impact the nature of
work leading to many new and improved job opportunities. Finally, I'll
examine IBM's position on the responsible and ethical use of AI.
The Evolution of AI
The idea of creating a `thinking' machine is not new and precedes
modern computing. The study of formal reasoning dates to ancient
philosophers such as Aristotle and Euclid. Calculating machines were
built in antiquity and were improved throughout history by many
mathematicians. In the 17th century Leibniz, Hobbes and Descartes
explored the possibility that all rational thought could be made as
systematic as algebra or geometry.
In 1950, Alan Turing, in his seminal paper Computing Machinery and
Intelligence, laid out several criteria to assess whether a machine
could be deemed intelligent. They have since become known as the
``Turing test.'' The term ``artificial intelligence'' was first
introduced in 1956, sixty-one years ago, and AI as an academic
discipline took off. Three years later, in 1959, IBM scientist Arthur
Samuel coined the term ``machine learning'' to refer to computer
algorithms that learn from and make predictions on data by building a
model from sample inputs, without following a set of static
instructions.
An algorithm is simply a set of rules to be followed in
calculations or other problem-solving operations. It can be as basic as
the steps involved in solving an addition problem or as complex as
instructing a computer how to perform a specific task. One type of
machine learning and AI algorithm that has gained tremendous attention
over the past several years is an artificial neural network. It has
been essential to the explosive growth of AI systems today.
Artificial neural networks are inspired by the architecture of the
human brain. They contain many interconnected processing units, called
artificial neurons, which are analogous to biological neurons in the
brain. Typically, neurons are organized in layers. Different layers may
perform different kinds of operations on their inputs. When presented
with sample data, an artificial neural network can be trained to
perform a specific task, such as recognize speech or images. For
example, an algorithm can learn to identify images that contain cars by
analyzing numerous images that have been manually labeled as ``car'' or
``no car.'' It can then use those results to identify cars in images
that it has not seen before.
Even though neural networks and other machine learning algorithms
were actively researched more than six decades ago, their practical use
was hindered by the lack of digitized data from which to learn from and
insufficient computational power. At the time, most data were in analog
form and not easily available to a computer. Training of the neural
network algorithm was and remains a computationally intensive process.
Due to the limitations of processors, it could not be implemented
effectively.
Over the last decade, the explosion of digital data, the growth in
processing speed and power, and the availability of specialized
processing devices such as graphical processing units (GPUs) have made
it possible to use artificial neural networks in real-world solutions.
Today, computation is carried out not only in the cloud and in data
centers, but also at the edge of the network, in sensors, wearable
devices, smart phones, embedded electronics, factory machines, home
devices, or components in a vehicle.
These conditions have also allowed researchers and engineers to
create incredibly complex neural networks, called deep learning
networks. They perform in ways comparable to humans in many tasks. For
certain tasks, such as speech and image recognition, game playing, and
medical image classification, these networks can outperform people.
Today, neural networks are used in a variety of applications, including
computer vision, speech recognition, machine translation, social
network analysis, playing board and video games, home assistants,
conversational devices and chatbots, medical diagnostics, self-driving
cars, and operating robots.
In addition to machine learning, AI systems deploy a variety of
other algorithms and technologies that include knowledge
representation, machine reasoning, planning and scheduling, machine
perception (speech and vision), and natural language processing and
understanding. At IBM, we are actively researching and advancing these
and other technologies so that we can continue to enhance AI systems.
We are also envisioning and developing the next-generation
infrastructure required for increasingly complex AI tasks and
workloads. This is the physical hardware required to run AI algorithms:
the processors, servers, databases, storage, data centers and cloud
infrastructure. When all these pieces are aligned in a way that allows
algorithms to analyze data with maximum efficiency, we refer to it as
the ``full stack.''
By successfully engineering the full stack, we can build AI-powered
solutions that we can apply to a broad array of societal and industry
challenges. While many tend to focus on the benefit of automation, we
believe that AI's true impact will be felt in assisting people's daily
lives, and by helping us carry out extremely complex tasks we cannot do
on our own. That includes everything from forecasting the weather, to
predicting how traffic will flow, to understanding where crops will
grow the best. AI will also help us research the best combinations of
compounds for drug development, repurpose chemical structures for new
medicines, and optimize vastly intricate supply chains. I'd like to
illustrate this further with a look at how IBM Watson is already being
used across a range of different industries.
AI applications to industries
My first example illustrates how AI can assist humans in reacting
to a problem when there is very little time to react. The security of
data and on-line transactions is fundamental to the growth of commerce.
But the simple fact is that most organizations can't keep up with the
threats. Security experts are too few and overstretched. Sophisticated
attacks, including those using AI tools, are coming at a rate that
makes them extremely difficult to stop. Entire networks are compromised
in the blink of an eye. Watson for cybersecurity allows us to turn the
tables. It sifts through the insights contained within vast amounts of
unstructured data, whether it's documented software vulnerabilities or
the more than 70,000 security research papers and blogs published each
year. It instantly alerts security experts to relevant information,
scaling and magnifying human cognition. It also learns from each
interaction that has an alert, and works proactively to stop continued
intrusion. Security analysts, armed with this collective knowledge, can
respond to threats with greater confidence and speed.
The second example shows how AI is enhancing customer experience.
Tax preparation is an area ripe for AI solutions. H&R Block is using
Watson to understand context, interpret intent and draw connections
between clients' statements and relevant areas of their tax return.
Watson is working alongside H&R Block Tax Pros as they take clients
through the tax return process, suggesting key areas where they may
qualify for deductions and credits. Clients can follow along and
understand how their taxes are being computed and impacted by numerous
aspects of the tax code. They can also see the many paths to file a
return with the IRS, pinpointing the route that delivers a maximum
refund.
A third example demonstrates AI's ability to personalize the client
experience. 1-800-Flowers launched an AI-powered gift concierge powered
by Watson Conversation. It interacts with online customers using
natural language. The service can interpret questions, then ask
qualifying questions about the occasion, sentiment and who the gift is
for to ensure that suggestions are appropriate and tailored to each
customer. In this way, the customer can get the right flower for the
right occasion.
The next example highlights AI's role in enhancing agricultural
productivity. A program led by our Research division called IBM PAIRS
is bringing the power of AI to improve crop yields. It works by
processing and analyzing vast amounts of geospatial data to generate
vastly improved long term weather and irrigation forecasts. Using these
methods, IBM Research and Gallo Winery co-developed a precision
irrigation method and prototype system that allows Gallo to use 20
percent less water for each pound of grapes it produces.
A final example shows AI's ability to optimize supply chains.
Traditional brick and mortar retailers are under tremendous pressure
from e-commerce. They must find new, cost-effective and efficient ways
to deliver goods to buyers in order to stay in business. That means
offering customers a range of delivery options--pick up in store, ship
from the nearest store, or move goods seamlessly between store and e-
commerce. Our client, a major American retailer, had to coordinate this
effort across a thousand stores in their fulfillment chain. Our
predictive models enabled them to determine optimal distribution across
their entire chain, factoring in dozens of different variables. Over an
eight-day period including Black Friday and Cyber Monday, they
processed more than 4 million orders--a company record--at a savings of
19 percent per order compared to the prior year. This led to an overall
savings of $7.5 million dollars.
From cybersecurity, to customer experience, to personalization, to
productivity and optimization, AI is playing a bigger and bigger role
in all realms of commerce. And its uses will only grow.
AI and blockchain
The potential for AI becomes even greater when combined with other
emerging technologies such as blockchain. Blockchain stores data in
shared, secure, distributed ledgers that allow every participant
appropriate access to the entire history of a transaction using a
``permissioned'' network--one that is highly secure and can determine
who can see what. Blockchain holds the promise to be the way we may do
all transactions in the future.
A typical AI process could include data collection, use of the data
to train a neural network and then deployment of the pre-trained model
to power the application. Blockchain supports the AI process by
reducing the risk of data tampering and provides data in a form that
can be used and audited. There's an old saying in the computer industry
``garbage in, garbage out,'' and that applies to data and how you use
it. The integrity of the data used as input to the AI model is a
necessary criterion in ensuring the value and usefulness of the model.
Because it can process and analyze massive quantities of data, AI
can use blockchain data to gain valuable insights and detect patterns
in how supply chains work and processes behave. Over time, this will
generate a valuable source of clean, trusted transactional data that
cuts across industries to give us new insights. That includes both
structured and unstructured data--everything from Internet of Things
(IoT) information to compliance and geospatial data that's stored on a
blockchain. AI can use this information to generate valuable insights
and detect patterns in near-real time, driving new efficiencies across
business operations.
For example, IBM Research is working with Everledger, a company
that tracks and protects diamonds and other valuables. We're using AI
to analyze digital information on one million diamonds Everledger has
stored on a blockchain. We can cross-check that data against UN
regulations to prevent the sale of conflict diamonds. We can verify
time and date stamps. We can certify laser inscriptions in the girdle
of the stone. We can perform these analytics directly on the
blockchain, without the need to extract the data first. This minimizes
opportunities for data tampering and fraud. While this is a specialized
application, it shows some of the kinds of data we can collect and
analyze at huge scale.
We have also partnered with Walmart to use blockchain and AI
techniques to ensure food safety. Today's food supply chains are highly
complex and involve multiple components, stakeholders, and activities.
This complexity makes it difficult to identify sources of
contamination, counterfeit substitutions, loss of refrigeration, or
food transportation safety issues as products move from their sources
to their consumption by consumers. Blockchain supports traceability by
tracking the food products from origin to destination and by allowing
certification of respective transactions and events along the way. AI-
powered technologies are used to analyze this information to help
ensure food that can be eaten safely.
AI and the Future of Work
Artificial intelligence will alter the way people work. This has
been true of many new technologies that have benefited human
populations over time because they dramatically improved industrial
output. They have led to fewer grueling jobs. In the process, new types
of jobs have emerged. However, such disruptive improvements have always
called for a period of training and adjustment.
We need to openly understand and recognize this fact, so that we
can create the right conditions to make this transition as successful
as possible. As a nation, we need to be prepared to offer the
appropriate education and support to manage this change well. There's
no question the advent of artificial intelligence will impact jobs.
Despite the fear, anxiety, and prediction of massive job loss, history
suggests that, even in the face of technological transformation,
employment continues to grow and very few occupations disappear.
Rather, it is the transformation of occupations that is very likely
to be widespread that will impact most workers. Occupations are made up
of tasks. It is the tasks that are automated and reorganized where the
transformation occurs. Workers will need new skills for the new
transformed tasks and occupations. But, it is the tasks that cannot or
will not be automated where workers provide the greatest value,
commanding higher wages and incomes as a result.
Some ``new collar jobs'' will emerge--jobs that require advanced
technical skills but do not necessarily require a full undergraduate
education. A study by Accenture of more than 1,000 large companies that
are already using or testing AI and machine-learning systems identified
the emergence of entire categories of new, uniquely human jobs with no
precedents.
For example, ``trainers'' will be required to teach AI systems how
they should perform. They may write scripts for chatbots, helping them
to improve their understanding of human communication, or help provide
labeled data needed to train the algorithm. They may teach AI systems
how to personalize their recommendations, or show compassion when
responding to certain problems that require it. Another category of
``explainers'' will be needed to help convey how AI systems have
arrived at a decision or a recommendation. They'll monitor the
operations of AI systems or perform forensic analyses on algorithms and
make corrections to them if they generate an incorrect result. Earlier,
I referenced the shortage of qualified cybersecurity professionals. In
the future, we'll need far more of them to engage with AI systems,
review the recommendations these systems offer and act decisively upon
threats.
There are actions we must take now to ensure the workforce is
prepared to embrace the era of AI and the ways it will augment our
economy. To begin, we must address the shortage of workers with the
skills needed to make advances in AI, create new solutions and work in
partnership with AI systems. We need to match skills education and
training with the actual skills that will be required in the emerging
age of AI.
At IBM, we have an educational model called P-TECH to train new
collar workers for a job in technology. P-TECH combines the best of
high-school, community college, hands-on-skills training and
professional mentoring, and provides public high school students in
grades 9-14 a path to post-graduation opportunities in fields aligned
with the skills American employers are looking for.
Our goal must be to create multiple pathways like this for more
people to acquire the skills that will be in demand, as AI use becomes
more commonplace. We can use the example of the adoption of software
programming as a critical skill that is taught in many high school and
colleges. Some colleges require that all students learn how to code
since they consider it a necessary skill for success. Students becoming
proficient in programming have a wider range of job opportunities.
In the future, we may promote and see a similar trend with students
gaining understanding of and proficiency in AI techniques such as
machine learning. Preparing more U.S. students and workers for success
in these well-paying new collar jobs is essential if we want a
workforce that is ready to capitalize fully on AI's economic promise.
Let me also say that as well-intentioned as it may seem to some,
taxing automation will not serve the cause of fostering employment in
the new AI economy. It will only penalize technological progress. We
should not adopt measures like this one that will harm America's
competitiveness.
Inevitably, people adapt best by finding higher value in new
skills. Technologies that are easiest to integrate and integrate with
will be those that improve human productivity. But they should not
replace human judgment. IBM Watson was designed from the beginning to
work in concert with human expertise. It will only be successful as
long as there are people with the right skills to engage with it.
Building trust in AI
To enjoy the full benefits of AI, we will also need to have
confidence in the recommendations, judgments and uses of AI systems.
IBM is deeply committed to the responsible and ethical development of
AI. Last year, we published one of the first corporate white papers on
this subject. The paper, which was intended to help launch a global
conversation, centered around the need for safe, ethical, and socially
beneficial management of AI systems.
Trust in automated systems is not a new concept. We drive cars
trusting the brakes will work when the pedal is pressed. We perform
laser eye surgery trusting the system to make the right decisions. We
have automated systems fly our airplanes trusting they will navigate
the air correctly. In these cases, trust comes from confidence that the
system will not make a mistake, leveraging system training, exhaustive
testing, and experience. We will require similar levels of trust for AI
systems, applying these methodologies.
In some cases, users of AI systems will need to justify why an AI
system produced its recommendation. For example, doctors and clinicians
using AI systems to support medical decision-making may be required to
provide specific explanations for a diagnosis or course of treatment,
both for regulatory and liability reasons. Thus, in these cases, the
system will need to provide the reasoning and motivations behind the
recommendation, in line with existing regulatory requirements specific
to that industry. In the European Union, this will be a requirement for
all automated decision-making AI systems as of May 2018.
These safeguards can also help to manage the potential for bias in
the decision-making process, another important concern with AI. Bias
can be introduced both in the datasets that are used to train an AI
system and by the algorithms that process that data, and how people
interpret and communicate the discerned insights. Our belief is that
the data and algorithmic aspects can not only be managed, but also that
AI systems themselves can help eliminate many of the biases that
already exist in human decision-making models today.
At the beginning of this year, IBM issued principles for
transparency and trust to guide our development and use of AI systems.
In summary, they state the following:
We believe AI's purpose is to augment human intelligence
We will be transparent about when and where AI is being
applied, and about the data and training that went into its
recommendations.
We believe our clients' data and insights are theirs.
We are committed to helping students, workers, and citizens
acquire the skills to engage safely, securely, and effectively
with cognitive systems, and to do the new kinds of work that
will emerge in an AI economy.
In the same way that we are committed to the responsible use of AI
systems, we are committed to the responsible stewardship of the data
they collect. We also believe that government data policies should be
fair and equitable and prioritize openness.
IBM is actively innovating in this field. We are deriving best
practices for how, when, and where AI algorithms should be used. We are
creating new AI algorithms that are more explainable and more accurate.
We are working on the algorithmic underpinnings of bias and AI, such as
creating technologies that can identify and cleanse illegal biases from
training datasets.
Of course, no single company can guarantee the safe and responsible
use of such a pervasive technology. For this reason, IBM is a founding
member of the Partnership on AI, a collaboration with other key
industry leaders and many scientific and nonprofit organizations. Its
goal is to share best practices on AI technologies, advance the
public's understanding, and serve as an open platform for discussion
and engagement about AI and its influences on people and society.
AI has enormous transformative power. Much has been said about its
potential to transform sectors and industries. However, AI is also
giving us a technological toolkit to address many societal challenges.
At IBM we are committed to pioneering new solutions, and showcasing and
promoting the opportunities to use AI in social good applications.
Three years ago, we launched the AI for Social Good program and have
executed a number of AI for Good projects, from using AI to understand
patterns of opioid addiction, to prototyping recommendation systems
that would aid low-income individuals and help them stay out of
poverty, to applying machine learning to understand transmission
mechanisms of the Zika virus.
Earlier this year, we announced the MIT-IBM Watson AI Lab, a
partnership with Massachusetts Institute of Technology (MIT) to carry
out fundamental AI research. One of the research areas for the lab is
focused on advancing shared prosperity through AI--exploring how AI can
deliver economic and societal benefits to a broader range of people,
nations and enterprises.
Lastly, no discussion of the future of AI would be complete without
acknowledging the critical role of government. Public investment and
policy support have been the twin pillars of American global
technological leadership for the past half-century. We hope and expect
the same will be true in the coming age of AI. For this reason, we
enthusiastically welcome the interest and support of the United States
Senate as this technology continues to evolve. Together, we can ensure
that AI serves people at every level of society and advances the common
good.
Senator Wicker. Thank you very much.
Dr. Felten.
STATEMENT OF DR. EDWARD W. FELTEN, Ph.D.,
ROBERT E. KAHN PROFESSOR OF COMPUTER SCIENCE
AND PUBLIC AFFAIRS, PRINCETON UNIVERSITY
Dr. Felten. Chairman Wicker, Ranking Member Schatz, and
members of the Subcommittee, thank you for the opportunity to
testify today.
Progress in AI has accelerated over the last decade.
Machines have met and surpassed human performance on many
cognitive tasks, and some longstanding grand challenge problems
in AI have been conquered.
Recent experience during this time teach us some useful
lessons for thinking about AI as a developing technology.
First, AI is not a single thing; it's different solutions
for different tasks. Success has come in ``narrow AI,'' which
applies a toolbox of specific technical approaches to craft
solutions for specific applications. There has been a lot less
progress on general AI, which tries to create a single, all-
purpose artificial brain, like we see in the movies.
Second, successful AI doesn't think like a human. If it is
an intelligence, it is sort of an alien intelligence. AI and
people have different strengths and weaknesses, so teaming up
with AI is promising if we can figure out how to work with an
intelligence different from our own.
And, third, more engineering effort or more data translates
into better AI performance. Progress requires a lot of hard
work by experts, and that's why our AI workforce is so
important.
The strategic importance of AI to the United States goes
beyond its economic impact to include cybersecurity,
intelligence analysis, and military affairs as well.
The U.S. is currently the world leader in AI research and
applications, but our lead is not insurmountable. Countries
around the world are investing heavily in AI, so our industry
researchers and workforce need support in their efforts to
maintain American leadership in this area. American companies
recognized the potential of AI early on and have been investing
and moving aggressively to hire top talent.
Our lead in research and development is less secure.
Federal funding for AI research has been relatively flat.
Aggressive hiring by industry has thinned the ranks of the
academics who train the next generation of researchers.
Industry does a lot of valuable research, but the public
research community also plays an important role in basic
research and in training young researchers, so investments in
policies to support and grow the public research community are
important.
Policies to enhance access to high-quality education for
all American children, especially in computing, lay the
foundation for our future workforce. And America has always
been a magnet for talent from around the world, and that has to
continue if we are to retain our leadership.
The many benefits of AI are tempered by some challenges. AI
systems may pose safety risks, they may introduce inadvertent
bias into decisions, and they may have unforeseen consequences.
Much of the criticism of AI has centered on the risk of
inadvertent bias, and real-world examples of biased AI are well
documented.
The good news is that there are technical ways to eliminate
bias. Developers can improve datasets to be more representative
of the population, they can use algorithms that are more
resistant to bias. Promising results on debiasing both data and
algorithms are emerging from the research community, and that
research should continue to be supported because it points a
way to deploying AI more widely with less concern about bias.
In considering the risks of AI, it's important to remember
that the alternative to relying on AI is to rely on people, and
people are also at risk of error and bias. In the long run, AI
systems will devise complex data-driven strategies to pursue
goals, but people will continue to decide which goals the
system should pursue. To better hold AI systems accountable, we
need new technologies and new practices to connect AI with the
human institutions that will govern it.
Regarding regulation, there is no need to create special
regulations just for AI at this time. In sectors that are
already regulated, the existing regulations are already
protecting the public, and regulators need only consider
whether and how to adjust the existing regulations to account
for changes in practices due to AI. For example, the Department
of Transportation, in the previous administration and this one,
has been adapting vehicle safety regulation to enable safe
deployment of self-driving cars.
Government agencies have important roles to play beyond
regulation. More expertise, advice, and coordination is needed
across the government to help agencies decide how to adapt
regulations and use AI in their operations. New structures and
new policies to strengthen this expertise would be very
beneficial.
With good policy choices and the continued hard work and
investment of American companies, researchers, and workers, AI
can improve the health and welfare of Americans, boost
productivity and economic growth, and make us more secure.
Americans currently lead the world in AI. We should not
step on the brakes; instead, we should reach for the
accelerator and the steering wheel.
Thank you for the opportunity to testify today.
[The prepared statement of Dr. Felten follows:]
Prepared Statement of Edward W. Felten Robert E. Kahn Professor of
Computer Science and Public Affairs, Princeton University
Chairman Wicker, Ranking Member Schatz, and members of the
Committee, thank you for inviting me to speak today about how best to
realize the benefits of artificial intelligence.
Artificial Intelligence (AI) and Machine Learning (ML)
Artificial intelligence (AI) and machine learning (ML) have been
studied since at least 1950. There has been an unexpected acceleration
in technical progress over the last decade, due to three mutually
reinforcing factors: the availability of big data sets, which are
analyzed by more powerful algorithms, enabled by faster computers. In
recent years, machines have met and surpassed human performance on many
cognitive tasks, and some longstanding grand challenge problems in AI
have been conquered.
Industry has recognized the rise of AI as a technical shift as
important as the arrival of the Internet or mobile computing. Companies
around the world have invested heavily in AI research and development,
and leaders of major companies have described adoption of machine
learning as a bet-the-company opportunity.
The strategic importance of AI/ML to the United States goes beyond
its economic impact. These technologies will also profoundly affect the
future of security issues such as cybersecurity, intelligence analysis,
and military affairs.
Fortunately, the United States is currently the world leader in AI/
ML research, development, and applications, in both the corporate and
academic spheres. Our national lead is not insurmountable, however.
Countries around the world are investing heavily in AI/ML, so our
scientists, engineers, and companies need support in their efforts to
maintain American leadership.
The Nature of AI/ML Today
The history of AI teaches some important lessons that are useful in
considering policy choices.
AI is not a single thing--it is different solutions for different
tasks. The greatest progress has been in ``narrow AI,'' which applies a
toolbox of specific technical approaches to craft a solution specific
to one application or a narrow range of applications. There has been
less progress on ``general AI,'' which strives to create a single, all-
purpose artificial brain that could address any cognitive challenge and
would be as adaptive and flexible as human intelligence. Indeed, there
is no clear technical path for achieving general AI, so it appears that
for at least the next decade the policy focus should be on the
implications of narrow AI.
In a world of narrow AI, there will not be a single moment at which
machines surpass human intelligence. Instead, machines may surpass
human performance at different times for different cognitive tasks; and
humans might retain an advantage on some cognitive tasks for a long
time. Even if machines surpass humans in the lab for some task,
additional time and effort would need to be invested to translate that
advance into practical deployment in the economy.
Successful AI does not think like a human--if it is an
intelligence, it is an alien intelligence. Because AI solutions are
task-specific and do not directly mimic the human brain, AI systems
tend to ``think'' differently than people. Even when successful, AI
systems tend to exhibit a different problem-solving style than humans
do. An AI system might handle some extremely complex situations well
while failing on cases that seem easy to us. The profound difference
between human thinking and AI operation could make human-AI teaming
valuable, if the strengths of people and machines can complement each
other. At the same time, these differences create challenges in human-
AI teaming because the teammates can have trouble understanding each
other and predicting their teammates' behavior.
On many cognitive tasks, more engineering effort or more data
translates into better AI performance. Many AI systems learn from data.
Such systems can be improved by re-engineering them to learn more from
the available data or by increasing the amount of data available for
training. Either way, devoting more effort to engineering and operating
an AI system can improve its performance. Machines are generally worse
than humans at learning from experience, but a machine with a very
large data set has much more ``experience'' from which to learn. Using
the narrow AI approaches that have been successful so far, expert AI
developers must invest significant effort in applying AI to each
specific task.
Benefits of AI/ML
AI is already creating huge benefits, and its potential will only
grow as the technology advances further.
For example, AI is a key enabler of precision medicine. AI systems
can learn from data about a great many patients, their treatments, and
outcomes to enable better choices about how to personalize treatment
for the particular needs, history, and genetic makeup of each future
patient.
AI is also enabling self-driving cars, which will eventually be
much safer than human drivers, saving thousands of American lives every
year. Self-driving vehicles will improve mobility for elderly and
disabled people who cannot drive and will lower the cost and increase
the convenience of transporting people and goods.
Given the tremendous benefits of AI in these and other areas and
the likelihood that the technology will be developed elsewhere even if
the United States does not lead in AI, it would be counterproductive to
try to stop or substantially slow the development and use of AI. We
should not ask the industry and researchers to slam on the brakes.
Instead, we should ask them to use the steering wheel to guide the
direction of AI development in ways that protect safety, fairness, and
accountability.
Policies to Support AI Progress
America's leadership in AI has been driven by three factors: our
companies, our researchers, and our talented workforce.
American companies recognized the potential of AI early on and have
been investing heavily in AI and moving aggressively to hire top
talent. This is the area in which our national leadership in AI seems
safest, at least in the short run. In the longer run, however, industry
must be able to work with world-leading American researchers and
workforce to sustain its advantage.
Our lead in research and development is less secure. Federal
funding for AI research and development has been relatively flat, even
as the importance of the field has dramatically increased. Aggressive
hiring by industry has thinned the ranks of the academic researchers
and teachers who are needed to train the next generation of leaders.
Although industry has carried out and supported a great deal of
research, it cannot and does not cover the full spectrum. The public
research community plays an important role in basic research, in
research areas such as safety and accountability, and in training young
researchers, so investments and policies to support and grow that
community are a key enabler of continued American leadership.
The foundations of the future workforce are laid in our K-12
schools. Policies to enhance access to high-quality education for all
American children, especially in computing, can grow the number of
students who enter higher education eager and able to pursue studies in
technical fields such as AI.
The American AI workforce has also been boosted immeasurably over
the years by the attractiveness of our universities and industry to the
most talented people from around the world. America has been a magnet
for talent in AI and other technical fields, and that must continue if
we are to retain our leadership. Policies to ensure that America
remains an attractive place for foreign-born experts to live, study,
work, and start companies are among the most important steps for the
future health of our AI enterprise.
Risks and Challenges of AI/ML
The benefits of AI are tempered by some risks and challenges: AI
systems may pose safety risks; they may introduce inadvertent bias into
decisions; and they may suffer from the kinds of unforeseen
consequences brought on by any novel, complex technology. These are
very serious issues that require attention from policymakers, AI
developers, and researchers.
Much of the criticism of AI/ML systems centers on the risk that
adoption of AI/ML will lead inadvertently to biased decisions. There
are several ways this could happen. If a system is trained to mimic
past human decisions, and those decisions were biased, the system is
likely to replicate that bias. If the data used to train a system is
derived from one group of people more than another, the result may
serve the overrepresented group to the detriment of the
underrepresented group. Even with ideal data, statistical artifacts can
advantage larger groups to the detriment of smaller ones. Real-world
examples of these sorts of biases are well-documented.
The solution is not to stop pursuing AI, but rather to take steps
to prevent and mitigate bias. Practitioners should work to improve
their data, to ensure that datasets are representative of the
population and do not rely on past biased decisions. They should also
improve their algorithms by developing and using AI systems that are
more resistant to bias, so that even if flaws remain in the data, the
system can produce results that are more fair. In both areas, data
improvement and algorithm improvement, the research community is
producing promising early results that will improve the anti-bias
toolkit available to practitioners. A robust national AI research
effort should include studies of algorithmic bias and how to mitigate
it.
In considering the risks of bias and accountability in AI, it is
important to remember that in most cases the alternative to relying on
AI is to rely on human decisions, which are themselves at risk of
error, bias, and lack of accountability. In the long run, we will
likely rely much more on algorithms to guide decisions, while retaining
the human role of determining which goals and criteria should guide
each decision.
Accountability, Transparency, and Explainability
The importance of the decisions now made or assisted by AI/ML
systems requires that the systems and their operators are accountable
to managers, overseers, regulators, and the public. Yet accountability
has proven difficult at times due to the complexity of AI systems and
current limitations in the theory underlying AI. Improving practical
accountability should be an important goal for the AI community.
Transparency is one approach to improve accountability. Disclosing
details of a system's code and data can enable outside analysts to
study the system and evaluate its behavior and how well the system
meets the goals and criteria it is supposed to achieve. Full
transparency is often not possible, however. For example, a system's
code might include valuable trade secrets that justify withholding
aspects of its design, or the data might contain private information
about customers or employees that cannot be disclosed.
Even where transparency is possible, it is far from perfect as an
accountability mechanism. Outside analysts may have limited practical
ability to understand or test a system that is highly complex and meant
to operate at very large scale. Indeed, even the designers of a system
may struggle to understand the nuances of its operation. Computer
science theory says that examining a system beforehand cannot hope to
reveal everything the system will do when it is exposed to real-world
inputs. So transparency, though useful, is far from a complete solution
to the accountability problem.
Another approach to accountability is inspired by the field of
safety engineering. The approach is to state clearly which safety,
fairness, or compliance properties a system is designed to provide, as
well as the operating conditions under which the system is designed to
provide those properties. This is backed up with detailed evidence that
the system will have the claimed properties, based on a combination of
design reviews, laboratory testing, automated analysis tools, and
safety monitoring facilities in place during operation. Rather than
revealing everything about how the system works, this approach focuses
on specific safety, fairness, or compliance requirements and allows
system developers to use the full range of technical tools that exist
for ensuring reliable behavior, including the tools that the system
developers will already be using internally for quality control.
Much needs to be done to make this approach feasible for routine
use. Research can develop and test different approaches to proving
behavioral properties of systems. Professionals can convene to develop
and pilot best practices and standards. The overarching challenge is to
understand how to relate the technical process of engineering for
reliable operation to the administrative processes of management,
oversight, and compliance.
Regulation and the Role of Government Agencies
There is no need to create special regulations for AI. Where AI is
used in sectors or activities that are already regulated, the existing
regulations are already protecting the public and regulators need only
consider whether and how to adjust the existing regulations to account
for changes in practices due to AI.
For example, the Department of Transportation (DOT) and National
Highway Traffic Safety Administration (NHTSA) have taken useful steps,
under the previous and current Administrations, to clarify how existing
safety regulations apply to self-driving vehicles and how Federal
safety regulations relate to state vehicle laws. These changes will
serve to smooth the adoption of self-driving vehicles which, once they
are mature and widely adopted, will save many thousands of lives.
Similarly, the Federal Aviation Administration (FAA) has been
striving to adapt aviation regulations to enable safe, commercial use
of unmanned aerial systems (UAS, or ``drones''), which have benefits in
many sectors, such as agriculture. The FAA has taken some steps to
increase the flexibility to use UAS commercially, but the interagency
process on UAS has been moving slowly. Agencies should be urged to work
with the FAA to advance this important process.
Government agencies have important roles to play beyond regulation.
For example, the National Institute of Standards and Technology (NIST)
and the Department of Commerce can contribute by setting technical
standards, codifying best practices in consultation with the private
sector, and convening multi-stakeholder discussions, much as they have
done in the area of cybersecurity.
All agencies should consider how they might use AI to better
accomplish their missions and serve the American people. AI can reduce
costs, increase efficiency, and help agencies better target their use
of taxpayer dollars and other limited resources. The National Science
and Technology Council's subcommittee on Machine Learning and AI can
serve as a focal point for interagency coordination and sharing of
ideas and best practices.
With good policy choices and the continued hard work and investment
of American companies, researchers, and workers, AI can improve the
health and welfare of Americans, boost productivity and economic
growth, and make us more secure. Americans currently lead the world in
AI. We should not step on the brakes. Instead, we should reach for the
accelerator and the steering wheel.
Thank you for the opportunity to testify. I look forward to
answering any questions.
Senator Wicker. Great. Thank you so much.
Let me start with Dr. Gil. Machine learning and artificial
intelligence capabilities are accelerating, and I think
Americans listening today or maybe insomniacs listening at 2 in
the morning 2 weeks from now on C-SPAN, know that we use AI for
social media and online search queries and smartphone apps. Ms.
Espinel mentioned health diagnosis, and I think she grabbed our
attention there.
What other industries, Dr. Gil, stand to benefit the most
from what we're talking about today?
Dr. Gil. I believe actually artificial intelligence is
going to touch every profession and every industry, but just to
give some concrete examples.
Senator Wicker. OK, good.
Dr. Gil. From security and cybersecurity, there's a class
of category of problems that have to do with responding with
low latency. So the nature of the problem that one has to
address is too complex, there are too many dimensions of it,
and AI can assist a professional detect a threat and be able to
assess the proper response. So sometimes it has to do with how
much time one has to make a decision, and can you be assisted?
In other areas in which, for example, in the health care
profession, even though we may have more time in some occasions
to perform a diagnosis or select a treatment, just the sheer
complexity of the number of documents, or in this case, it
could be genomic information that comes into play, goes beyond
the expertise that any given person can have. So in that
instance, it can assist, for example, in the process of, you
know, medical diagnosis, as an example.
In agriculture, here we're talking about being able to
integrate sensory measurements from soil and weather data,
satellite data, to be able to predict what kind of irrigation
one may have to deploy to improve the productivity of it, to
give another example.
So I think that, you know, in every situation where we're
trying to integrate knowledge that comes from sometimes
measurements from the physical world and also bodies of
evidence that we've accumulated through our expertise, combined
with our own expertise, we facilitate every worker and
professional to be able to make those better decisions.
Senator Wicker. OK. Now, Ms. Espinel and to all members of
the panel, Ms. Espinel says that our government needs to talk
about three changes to policy: open data, more government
research, and prioritizing education and workforce development.
Also, Dr. Felten says there's no need to create special
regulations for AI.
Who wants to comment on this? Does anybody want to take
issue that we have or is everybody in agreement with all four
of these statements? Is there some room for nuances and
disagreements? Does anybody want to respond?
Yes, Mr. Castro.
Mr. Castro. Thanks. So I think one of the most important
things to look at in this space is it's about technology
adoption. When we're looking at AI, the big question for the
United States in terms of competitiveness is, are we going to
be the lead in terms of adopting this technology rapidly in our
industries before other countries do it in their industries?
Because that's going to determine U.S. competitiveness long
term.
So when we're talking about policies in this space, there
are two types. There are the policies that help accelerate
deployment and adoption of the technology and our R&D in this
space, and then there are the regulations that might slow down
adoption or, you know, kind of skew or realign how we do this
adoption.
So when we're comparing ourselves to Europe, for example,
which is also pursuing this, we have to ask the question: Why
aren't we doing what Europe is doing to accelerate adoption?
And, two, are we having smart regulations that allow us to
apply it in our industries kind of better, smarter, and faster
than they're doing?
Senator Wicker. Do you sort of agree with Dr. Felten, that
regulations may actually impede our development of AI?
Mr. Castro. I think in most cases, regulation that's
focused on AI specifically is probably misguided. If there's a
problem there, we need to look broader and say, Why is this
problem happening? And is it caused in human situations as
well?
Senator Wicker. Dr. Felten, have I mischaracterized
anything you've said?
Dr. Felten. No, you have not. I would agree with Ms.
Espinel's points. With regard to regulation, in addition to
sectoral regulation, there's an important role for agencies
sometimes to create new regulatory structures to allow more
activity, as the FAA has been working to do with drones. The
FAA has been working to create new rules which allow broader
commercial use of drones in the United States. And so although
that is a change to regulation, it's one that enables more
activity by the commercial sector.
Senator Wicker. Thank you very much.
Senator Schatz.
Senator Schatz. Thank you, Mr. Chairman.
Thank you for all of your testimony.
The way I see this is there's a competitiveness side of the
ledger, which I think is not easy to do, but relatively
straightforward. And Ms. Espinel's testimony spells out some of
the steps that we can take on a bipartisan basis to make sure
that we win that race internationally. That part is, again, not
easy, but relatively straightforward morally and as a matter of
policy.
Where I think it does get difficult is that I think I
quibble with you, Mr. Castro, in the sense that I don't think
we're--that the endgame here is just that we race as fast as we
can in all sectors without regard to consequences and view any
regulatory effort as contradictory to our national goals. I
think part of what we have to do is recognize that in areas
like health care and agriculture, it's a pretty much unalloyed
good to have more data, to save lives, to make agriculture more
productive, for instance. In defense, it's a little tricky. In
criminal justice and policing, it's extremely tricky.
And so I don't think anybody in the Senate is talking about
European style regulation. I think what we are saying is that
this is a complex area that's going to revolutionize society
and the way we interact with each other, the way machines
interact with each other, and with ourselves, and if we're not
careful, we could enshrine some of our thorniest societal
problems in datasets. And I'm not as persuaded, Dr. Felten, as
maybe you are, that we can program our way out of that.
And one of the challenges that I would just maybe ask, if
we can start with Dr. Felten and go down the line, I'm worried
about diversity in the industry. I think that to the extent
that you have software engineers and decisionmakers both at the
line level writing the code, but all the way up to project
management and the people who are wrestling with some of these
moral questions, are mostly white men, and I think that's not a
trivial thing because they're not thinking about biases in
policing. They may be thinking differently about autonomous
weapons.
And so I'm wondering how you view--and I don't think this
is a place for regulation, but I do think this is a place for
us, as a society, to grapple with if this is going to be
transformational and change everything, is it fair, is it
rational, to have only, or I should say predominantly, white
men in charge of setting up these algorithms that most of the
rest of society can't even access because it's all proprietary?
Dr. Felten.
Dr. Felten. Sure. With respect to the question of whether--
of the role of technology in addressing these issues of bias, I
think technology has an important role to play, but it can't be
the entire solution, as you suggested, Senator. What we need is
a combination of institutional oversight and governance with
technology. Technology can provide the levers, but we still
need institutions that are able to pull those levers to make
sure that the results are conducive to what our institutions
and our society wants.
With respect to the question about diversity in the
workforce, this is certainly an issue. The AI workforce is even
less diverse than the tech workforce generally. And it's
important to take efforts to improve that so we can put our
whole team on the field as a nation. And I commend groups like
AI for All that are working on that to you.
Senator Schatz. Dr. Gil and then Ms. Espinel, we have a
minute and 20 seconds left.
Dr. Gil. OK. Perhaps I could address the topic of bias
associated with these models because bias can be introduced at
the level of the dataset, as you properly pointed out, if the
data that has been collected, you know, is not representative
of the whole population, in this case, it's to make the right
assessments. You can introduce then a bias in the----
Senator Schatz. Well, just to be clear, it can be
empirically valid, right? I mean, the simplest example is
here's where crimes have been committed in the past, right? It
turns out Ferguson, right? Load that into the dataset, it's a
predictive algorithm, works every time. You go over there, you
find more and more crime, and it spins and spins. And then on
top of it, enshrining that bias in an algorithm, I'm not sure
that--it gets more permanent than it would be if it were up to
the individual judgment of sheriffs, VAs, people.
Dr. Gil. Yes. So this is a very active field of research in
which we're very active that actually has also to do with in
the cases in which you may have high degrees of prediction, but
you're incorporating protected variables or protected
individuals in the case of the assessment that you cannot use
because of law, that is a variable that you cannot incorporate.
So there are ways actually to perform the data science to
be able to achieve, you know, a prediction. I'm not----
Senator Schatz. But if a police department like deals with
a vendor, and they say, ``We've got a predictive algorithm, and
we can't show you how this predictive algorithm works,'' but in
back of that, you know, inside of that black box, they've got
census block stuff, they've got all kinds of stuff that you
would be not allowed to use in policing, how do we even know?
Ms. Espinel, how worried should I be about this?
Ms. Espinel. So we talk about bias, but I would actually--I
would love to address that as well because it's an issue I'm
really passionate about and focused on, and I know we're
running low on time here, but you also have sort of led into
explainability and accountability, and that's really important,
too. So I will try to briefly touch on both and I'm happy to
continue this conversation.
Senator Schatz. Yes.
Ms. Espinel. So in terms of bias, I think there are really
two parts of it. So part of it is how the AI systems are
trained, how they're built, in essence. And, obviously, as you
point out, data can be inaccurate or it can be incomplete, or
it can have its own biases that are going to skew the outcomes.
And there are a number of things that can be done right now to
try to help with that.
So part is making sure that data scientists that are
building them have the tools and the training to try to counter
that. Second, and you already raised this, I think this is
another area, or you have another reason why diversity in tech
is so important, and I think the more experience and background
you have at the table as AI systems are being trained, the more
helpful it's going to be to try to avoid that.
I think, third, as has been mentioned, there is a lot of
research going on in this area, so continuing to support and
invest in research that will help lessen the chances of bias in
AI is very important.
And last I would say, you know, to the extent bias is
discovered, obviously companies should be working immediately
to try to address that.
So I think there are a number of things that can and should
be happening now to try to counter that.
I think there's another part of this discussion, which
we've heard less about, which I think is really important,
which is how AI can be used, not trained and built, but how it
can be used to try to counter bias and to try to broaden
inclusion. And there are a number of really interesting
examples here both in terms of hiring practices and in terms of
broadening inclusion for people with diseases like--or people
with conditions like autism or people that are visually
impaired where AI can dramatically transform their ability to
interact with society and in workplaces. And so I think having
more discussion about how AI can and should be used to try to
lessen bias and to try to broaden inclusion is very important.
I could talk more. I know we're running low on time.
Senator Schatz. I think my time is up. I'll let the other
members ask their questions.
Senator Wicker. Senator Schatz's time is close to expiring.
[Laughter.]
Senator Wicker. But we'll take another round.
Senator Moran.
STATEMENT OF HON. JERRY MORAN,
U.S. SENATOR FROM KANSAS
Senator Moran. I have four questions, and I'm glad to know
that the standard has now increased beyond the 5 minutes.
[Laughter.]
Senator Moran. Let me quickly try to ask these four
questions. First about research and development in the Federal
role. A number of us on this Committee are members of the
Appropriations Committee. We would think of opportunities to be
supportive of this endeavor by funding of NSF, NIH, DoD. What
am I missing? Is there something out there that we ought to be
paying attention to from an appropriations process that
supports Federal research in this regard to AI? Don't pause
very long here.
Dr. Gil. No, well, in addition to the agencies you listed,
I think the DOE also has an important element of it in the
intersection of high-performance computing and artificial
intelligence and how the computational platform to support
historical approaches to be able to do things like modeling of
chemical processes and sophisticated approaches to do that to
combine it with the more statistical approaches that are being
enabled now with artificial intelligence and machine learning.
So I think being able to combine those two disciplines in the
context of the DOE would be very helpful as well.
Senator Moran. Thank you.
The significance--the difference between private research,
business research, and government research, where do we see the
focus? Let me say, where are the most dollars being spent? Is
the private sector more engaged than the Federal Government?
Dr. Gil. I would say at this point it would be fair to say
that in the private sector, certainly in the technology world,
AI is the single most important technology in the world today.
So--and the levels of investment that we're all making around
that is commensurate with that statement.
Senator Moran. Yes.
Dr. Felten. The private sector investment in AI research
and development is much larger than the Federal investment
currently. I would agree with the list of agencies that you
listed, Senator, and that Dr. Gil listed. And I'd also commend
to you the ``National AI Research and Development Strategic
Plan'' that was published last October that was put together by
the research agencies.
Senator Moran. Thank you very much.
As we attempt to promote STEM education, in that broad
phrase of ``STEM,'' is that sufficient to describe the kind of
intellectual and academic excellence that we need in order to
develop AI? Is it something more than just promoting science,
mathematics, engineering, and research, the traditional kinds
of STEM things, as we pursue support of education?
Ms. Espinel.
Ms. Espinel. So that is very important, and I think trying
to ensure that every child in every state, if they want to go
into tech, they have the skills to do that, and that's a real
viable, realistic career opportunity for them. I think that's
critical.
I think there are other things that could be very helpful
as well. So one of the things that we've been thinking about is
trying to modernize vocational training. So for, you know, not
just for--important for very young children learning as well,
but as young adults are coming out of school, then thinking
about where their career path could take them, I think there's
a lot that could be done to try to improve those programs as
well.
Senator Moran. If anyone has suggestions in addition to the
short time-frame that I have, that Senator Schatz didn't have,
please let me know. We'd like to figure out how we focus our
educational support in a way that adds this new dimension or
additional dimension to what kids in grade school, middle
school, are learning. They're the future.
Ms. Espinel. That's fantastic. Thank you.
Senator Moran. As a Kansan, I need to ask a question about
agriculture. This could be Dr. Gil, Ms. Espinel, Dr. Castro,
or, well, really any of you, Dr. Bethel, where is the research
taking--who are the leaders in research when it comes to
agriculture? Is it the universities or is it the private sector
again who is focused on large data and what it can mean to
increase productivity and efficiency and better on-the-farm
income? Who should I be talking to that's fully engaged in this
world?
Dr. Gil. Yes. I think there is wonderful work going on
across a number of universities, and we can--I can give you
some more details of some specific programs that are, you know,
very well tailored to this. But certainly in the private sector
there is a lot of activity that has had to do with focusing in
the instrumentation aspect and the measurement of fields,
particularly with satellite data, being able to combine also
very unique datasets.
As we've aggregated datasets in terms of I alluded before
in terms of like soil characteristics, you know,
evapotranspiration models that we have, weather data, and be
able to combine all these layers of data together to be able to
have accurate forecasts and prediction, and to the degree that
we have more autonomy also in the agricultural fields to be
able to, you know, irrigate with more precision--right?--or be
able to use fertilizers with more precision as well, the
combination of all of those factors is what is increasing
productivity and what it's enabled to do that.
Senator Moran. Dr. Felten, my final question. You indicate
in your testimony there is no clear technical path for
achieving general AI, you have narrow and general. Is what
you're telling me is that that's more science fiction, like
more of a James Bond movie than where we are today?
Dr. Felten. That is the case today. We can't rule out the
possibility that general AI may come along far in the future,
but from a policymaking standpoint, narrow AI is what we have,
and it, I think, should be setting the agenda. We should be
alert for the possibility that sometime down the road general
AI may come, but it's not close.
Senator Moran. Thank you all very much.
Thank you, Mr. Chairman.
Ms. Espinel. Senator Moran, if I may, you mentioned
advances in farming technology, and since you are from Kansas,
I just wanted to let you know that we put out a study earlier
this year looking at the impact of software across the United
States, and Kansas is one of the states we saw the biggest jump
in jobs. So over 30 percent growth in software jobs in Kansas,
and you're up to nearly 40,000, and part of that is farming
technology, but it is other types of software services as well.
So Kansas is doing great.
Senator Moran. I wouldn't want to forget the aviation world
that we live in, too, in Kansas. Thank you very much.
Senator Wicker. Senator Peters.
Senator Peters. Thank you, Mr. Chairman.
Senator Wicker. How's Michigan doing?
Senator Peters. Yes, how's Michigan doing?
Ms. Espinel. Michigan is doing really, really well.
Senator Wicker. But not as good as Kansas, I'm sure.
[Laughter.]
Ms. Espinel. Well, Kansas did see a huge jump, but
Michigan, $13 billion in GDP from software into Michigan. And
Michigan is definitely doing better in jobs overall in terms of
numbers, maybe not quite as big a jump year to year, but
Michigan is a really--is a really strong state, and our----
Senator Wicker. And I guess New Mexico really isn't even in
the game.
[Laughter.]
Ms. Espinel. We actually had an event in Detroit last week
through our foundation talking about software and tech, and
very focused on the educational system in Michigan and the
great things that Michigan is doing to try to advance software
and technology in the state, so thank you.
Senator Wicker. Senator Peters.
STATEMENT OF HON. GARY PETERS,
U.S. SENATOR FROM MICHIGAN
Senator Peters. Thank you, Chairman Wicker, for bringing up
that question so I didn't have to use my time for that. That
was very well done.
[Laughter.]
Senator Peters. And you're right, Michigan is moving very
aggressively in this area, and it's primarily driven by self-
driving cars and what's happening in that space, which is very
exciting, something that I've been intimately involved in over
the last few years and months. And we now have some significant
legislation moving forward for that. In fact, it's been
described to me by folks in the AI space that having self-
driving vehicles may be the Moonshot for artificial
intelligence, that when AI can pilot a car through a complex
city environment with all sorts of things happening all around
it, that means AI has developed to the point where it's going
to be transformative in every single industry. It's going to be
a key barometer of where we are going forward. So we are
pleased that that's happening in Michigan.
In fact, we had General Mattis, who mentioned the four
places for technology in the country, and Michigan was one of
those four. So a little different vision than a lot of people
here in Washington may have for my great state, so I appreciate
the opportunity for that to come up.
The question I have--and I'm a believer in all of the
wonderful things that you're talking about. I believe this is
the most transformative technology in a long, long time, just
as it is in the auto industry. It's probably as big as when the
first car came off of the assembly line. We know what happened
after that, in creating the American middle class, changing
everything about our economy. We think the same thing will
happen with AI.
And so there's incredible promise for it, but I think we
also have to be very open to the potential downside to this.
And I know some of you have addressed the employment issue, and
I want to just talk about that because my experience has been
the folks who are big proponents of the technology downplay the
employment aspects. Folks who are scared probably overplay the
employment aspects. And the truth is going to be somewhere in
the middle.
And I think one thing it will have an impact on that
employment growth and what we've been seeing in the economy
recently is we have further concentration of industry and fewer
and fewer companies that have larger shares. That has actually
suppressed wage growth, it has created a less dynamic
environment when it comes to new business formation. I mean, we
can go through the economic arguments associated with that.
And so, Mr. Castro, you mentioned that whoever comes up--
whatever companies embrace AI will have a significant
technological advantage when they do that. We see that in the
auto industry. It's why the auto industry is racing to be
first, or at least very early, knowing that there is probably
going to be fewer car companies once AI is fully implemented as
well.
So my question to you and other panelists, what sort of
implications will AI have for the concentration of business in
those companies or those industries, or I should say those
individual companies within those industries, that have the
resources to be able to utilize this? And will it be more
difficult for small businesses?
Mr. Castro. Yes, so I think there are two effects. I mean,
if you talk to a company like Amazon, it's a--you know, they're
using AI more than anyone, and they're growing faster than
anyone.
And so in some cases, we're going to see--especially when
we're talking about global competitiveness, U.S. companies
growing because of the technology, and that that growth will
outpace the jobs offset. Of course, that won't happen
everywhere, and in many cases, what we want to see is more
productivity, which means fewer workers in a given space per
output.
In those cases, and what we've seen historically in this
phase, is that the new jobs are not necessarily AI jobs. It's
not that, you know, everyone is going to now be, you know,
building self-driving cars or designing them, it's that we see
more people in other professions, more doctors, you know,
classrooms with higher teacher-to-student ratios. The kinds of
changes we often say we want to see and can't pay for right
now, we can get in the future.
We did a really interesting study earlier this year that
was looking at occupational change over the last 165 years, and
it's actually at the lowest change, so the least disruption,
that we've seen in this entire time period. And the reason for
that is we get a lot of misperceptions when we see ATMs on the
corner, and you think there are fewer of these jobs, when, in
reality, you have more banks, you have more prosperity. And so
the job losses are usually more visible than the job creation,
which is why we have the skewed perception.
Senator Peters. But I think we go beyond job losses and
actually look at wage differentials and income inequality, and
that's probably what I was alluding to, is when you have
increased concentration, you have less dynamism in the economy,
which I think is consistent with what you just said about the
job churn has gone down, it's becoming less dynamic. Many
economists believe that's a big reason why we have growing
inequality in this country as well.
Certainly folks who are able to get these software jobs are
going to do extremely well, and God bless them for doing that,
but based on trends that we already see of stagnant wages, even
though there are increases in productivity, doesn't necessarily
translate into everyday wages for everyday folks, that that all
could accelerate at a very quick pace that we should be
thinking about, and I think it's important for us to be
conscious of that impact, it's not just jobs, it's income
inequality.
Now, Ms. Espinel, if you want to comment on that, please
do.
Ms. Espinel. I was just going to say briefly that, one, I
think you're right, it's something we need to be thinking
about. You said, or maybe you were alluding to what Mr. Castro
said, in terms of businesses using AI. I guess I would say, you
know, we don't think that big businesses or concentrated
businesses are the ones that should be using AI. I think our
hope is that AI and the technology behind it will be
democratized sufficiently so that small businesses will have
the ability to use it as well and help them in whatever their
business objectives are.
So I guess I would say, yes, I would agree. It would be a
concern if we saw AI being used primarily by just some large
companies, but I personally don't think that will be the future
of its deployment, and I know, certainly speaking for our
members, they would like for small businesses, for the public
sector, for any type of organization that is trying to make
decisions and trying themselves to make decisions in a way that
is better informed to be able to have the use of the benefits
of AI.
Senator Peters. Well, I would agree that that's the goal,
and we hope we could democratize it, but it hasn't necessarily
played out that way. It does take significant amount of
capital.
And as Mr. Castro mentioned, Amazon is using AI to the
greatest extent of any other company, it's growing the fastest,
and that's why we have brick-and-mortar retailers that are
going out of business all over the country as well. It's great
for Amazon, great for AI, great for productivity for Amazon,
but it may not be so great for the mom-and-pop store that's
there. And I would argue that mom-and-pop store thinks they
can't have an AI system like Amazon, that's just simply not
realistic for them.
And so we need to be thinking. I don't have the answers,
but I think we need to be thinking about that or we're going to
be facing some significant societal challenges in the future.
But thank you for your comments.
Senator Wicker. Senator Udall.
STATEMENT OF HON. TOM UDALL,
U.S. SENATOR FROM NEW MEXICO
Senator Udall. Thank you, Mr. Chairman.
And thank you to all the witnesses here today. I think this
has been very excellent testimony. And obviously there are some
very positive aspects to AI. But I wanted to ask about the bots
and the software used to hurt consumers. The New York Times
recently reported on so-called ``Grinch bots,'' software used
to predict the web links of the in-demand toys and other
merchandise and to purchase the goods before the general public
has access to these items. This practice has caused many of
this holiday season's most popular items, such as Fingerlings,
Super Nintendo Classic Edition, Barbie Hello Dreamhouse, to be
sold out online within seconds. However, one can go to eBay and
easily find these same products available for increased costs,
sometimes dramatically increased.
Have either of your organizations worked with retailers to
prevent software bots from taking advantage of Internet deals
to jack up the prices of goods? Can you identify other ways to
prevent these software bots from using machine learning to
become more and more sophisticated?
Ms. Espinel. I'm happy to take this as the parent of two
young boys that are busy filling out their Christmas lists.
So--and I actually think this is an example of an area where AI
can be really helpful. So Dr. Gil and others have talked about
the use of AI and cybersecurity because AI is really good at
looking at large amounts of data, detecting patterns, and then
predicting where threats might lie.
The Grinch bots I think is an area--AI is also great at
fraud detection, and there are a lot of the characteristics of
these Grinch bots that are similar. So part of it is, you know,
if you see--if you see unusual patterns like huge, large,
amounts of purchases that are happening very, very quickly, and
these Grinch bots work so they can process transactions in just
a few seconds, that is a pattern, and an unusual pattern.
And AI is really good at looking at patterns like this, and
then alerting the retailers so they can shut down those
purchases in much the same way that AI is being used now for
credit card fraud detection and can help detect unusual
patterns and then, you know, give your bank or you, as the
credit card user, the ability to say, ``I didn't approve those,
so you need to block my card.'' That--this type of activity I
think is actually a great example of how AI could be deployed
to try to detect those unusual patterns and then tell the
retailers to not process those transactions and shut it down.
Senator Udall. Yes, we hope that that happens more because
you can see the consumer damage, I think every day, and in a
lot of the business coverage.
Dr. Bethel, in your testimony, you spoke of the need to
access more and more data, including social media and mobile
device location history to help refine the uses of artificial
data. I'm concerned about the privacy implications that this
kind of sharing could have. Could you speak to the ways to
obtain relevant data while still protecting consumers'
sensitive information? And do others on the panel share my
concerns on privacy?
Please, Dr. Bethel.
Dr. Bethel. I mean, privacy, anytime you're dealing with
data, is going to be an issue of concern. And I think we have
to be responsible in how we use that data and how we obtain the
data. So there does probably need to be some consideration
given to how that data is obtained, what that information is
used for, and trying to ensure the privacy of the individuals
that that information is coming from. It's critical to the
success, I think, of AI, and being responsible with that.
So there may need to be some regulation in that to protect
the general public from harm basically is where regulation I
feel is needed. But we need to be aware of how that is
happening and try to put in place measures to ensure the safety
of that data.
Senator Udall. Great. Thank you.
Please.
Mr. Castro. Thank you. On the question of privacy, there is
actually some really interesting where we're seeing, you know,
if you look at kind of trends, increased use of AI can actually
increase privacy for individuals because it takes personal data
out of the hands of humans, which is what generally people are
concerned about. There are general privacy concerns when you
actually start talking to people, is that, ``I don't want you
personally to see my medical record. I don't want this person
over here to see my financial data or use it in a harmful
way.'' But when they say, you know, ``Are you OK with a
computer seeing it?'' suddenly everything is OK. And you see
this in a lot of sensitive areas. This is one reason why people
like to shop online for certain personal items, they don't want
to see a clerk at checkout, but they're absolutely fine with
Amazon having that information.
So in many cases, what we want to see is how we can have
policies that actually shift it so companies can guarantee the
data isn't ever touched by a human, it's only touched by a
computer, and that is a privacy guarantee. But then we need to
make sure we can actually hold companies accountable if they
ever allow that data to escape in certain ways.
Senator Udall. Yes.
Dr. Felten. AI raises the stakes in the privacy debate
because it boosts both the uses of your data that you might
like as well as the ones that you might not like. And so the
importance of protecting data privacy continues and maybe is
higher in a world with AI.
Dr. Gil. Yes. And I would just say from the perspective of
IBM, we take the strong view that our clients' data is their
own, and the insights that derive from the application of AI
when we help them to do that are their own, too. So we take a
very strong view of not using data for other purposes that are
not the intended ones.
Senator Udall. Great.
Ms. Espinel. I'll just say very briefly, I think it's
important to distinguish between different types of data and
different types of AI. Our BSA companies are at the forefront
of protecting privacy, and much of the AI that we built is not
built using personal data. So I think it's important to bear in
mind.
Senator Udall. Thank you very much.
I yield back, Mr. Chairman.
Senator Wicker. Senator Young.
STATEMENT OF HON. TODD YOUNG,
U.S. SENATOR FROM INDIANA
Senator Young. I thank our panelists for being here today.
I want to build on some previous line of questioning about our
labor markets and their preparation or lack thereof, as we
start to move into an AI-driven economy. What sort of questions
should policymakers be asking right now to ensure that we
optimize the skills our workforce has to the extent possible to
prepare for this, in many ways, exciting and promising new
technology?
Dr. Felten. Well, I think--I think there's one set of
questions with regard to education to make sure--especially in
the K-12 system, that kids are getting some basic education in
computer science. Things are more in hand, I think, at the
university level; it's more a matter of resources there. The
more difficult questions relate to adult works and displaced
adult workers and what is available in terms of retraining or
apprenticeship programs to help them to make sure that those
are available and to make sure that those are backed by good
data on the effectiveness of programs is important.
Ms. Espinel. So I would say I think there are three things
to think about. One is, you know, if you look at the situation
today, the Department of Labor BLS is saying that by 2020
there's going to be 1.4 million jobs that need a computer
science degree, and yet we only have about 400,000 graduates in
the pipeline. So we have a gap in our labor pipeline today that
definitely needs to be addressed if the United States is going
to stay competitive in this area. So that's one thing we need
to focus on.
Second is in terms of education. I think we need to be
rethinking our educational system for young people to ensure
that they have access to those skills and the opportunity to
acquire them so that if they decide that they want to go into
tech--and not every tech job requires a 4-year college degree,
and I think that's also something we need to be better about
explaining--that they have a realistic ability to do so. But
that is going to require some rethink of the educational system
that we have now. And I think we need to be modernizing the
vocational training programs we have for young people that are
coming out of school.
And then the third area, which Dr. Felten referred to, in
terms of people that are in the workforce now, I think we need
to not just be investing in reskilling and retraining programs,
but, again, thinking differently about them. I think we need to
do a better job of matching the skills that people have with
the employment needs that are out there across the country,
which is an area where I think there's a lot of work to be
done, but a lot of potential.
Senator Young. Yes. With respect to this issue of the labor
markets and AI, what sort of assumptions are you hearing or
reading about that you think either overstate some of the
forces that will be unleashed as AI continues to develop and be
adopted, perhaps strike you as a bit alarmist, or understate
these forces?
Dr. Gil. I think----
Senator Young. Yes, sir.
Dr. Gil. Oh, sorry. Dr. Felten was, you know, very helpful
in providing this journey from the narrow form of artificial
intelligence that we have today to a general form of artificial
intelligence that ultimately could perform, you know,
arbitrary, you know, tasks and domains. We're far away from
that. So very often the discussion gets framed in terms of
either the policy implications or the implications to the labor
market associated with that future form of artificial general
intelligence that frankly is decades away at best.
So in that sense, when the conversation is framed in that
lens, it does come across as alarmist--right?--you know. When
we talk about the more narrow form of artificial intelligence
that exists today, it's more of a focus of, what domains can it
have an impact? Where are proven examples that we can do better
on that? And at best, you will perform some narrow specific
tasks that can be complemented with human labor.
Senator Young. Mr. Castro, do you have some thoughts?
Mr. Castro. Yes. I mean, so the number one study that
everyone thinks about in terms of jobs is the study that came
out of Oxford through research of Frey and Osborne that said 47
percent of U.S. jobs would be eliminated by I think it was 2025
or 2030. That study was--first of all, it's not peer reviewed,
and when you look at the data, they use a very kind of, you
know, flawed methodology to come up with this estimate.
So they list in the back in their Appendix all the
different professions that they say will be automated with AI,
and it's things like fashion models and barbers, which they
tried to walk a robot down the runway in Japan once, and they
haven't done it since.
So, I mean, you know, kind of realistically, those numbers
are very much inflated, and they're not actually tied to what
we're seeing in the market today. So, you know, the main thing
is I think when you see these studies, and there have been a
number of studies that have used their data, it doesn't reflect
reality.
Senator Young. I'll just close here and indicate, you know,
the reason we're focused on this I think is we see some
incredible potential here. There are some studies that indicate
AI has the potential to increase the rate of economic growth in
the U.S. from 2.6 percent to 4.6 percent by 2035. I mean,
that's just amazing. That would benefit all Americans. There
are some serious policy things we need to wrestle with.
I've been partnering with Senator Cantwell, who has a lot
of expertise and professional background in the area of
technology, and we've developed legislation that would
establish a Federal advisory committee to help us better
understand some of these issues. So if you have thoughts moving
forward about things that a Federal advisory committee should
look at as we consider the policy implications and broader
market implications of AI, I'd certainly welcome those, and I
suspect Senator Cantwell would as well.
So thank you, Mr. Chairman.
Senator Wicker. What a nice segue to Senator Cantwell.
Senator Young. Yes.
Senator Wicker Senator Cantwell is now recognized.
STATEMENT OF HON. MARIA CANTWELL,
U.S. SENATOR FROM WASHINGTON
Senator Cantwell. Thank you, Mr. Chairman. And I do look
forward to working with Senator Young on this issue and the
various aspects of investigation.
I wanted to bring up applications because one of the things
that I think we should be thinking about is our role as an
actual user. And one of the things I'm most interested in is
AI's application for cybersecurity. One of our biggest threats
obviously we face now is the threat of cybersecurity in all
sorts of ways. I've seen some applications by MIT and I think
University of Louisville, several entities that are both
finding fault in code, basically doing a better job--why wait
to find out some guy forgot to do the Apache patch, forgot to
do the patch? You know? I mean, one employee at that company
cost everybody a lot of money because he didn't put in a patch.
AI could help us find errors in code or actually in some of
these areas I think predict cyber attacks.
So I don't know who on the panel could speak to that, but
to me, one of the applications that I hope that we will look at
is the government's use of this as it relates to combating
cybersecurity.
Dr. Felten. There's a huge opportunity there, and it's
rapidly becoming a necessity, as the bad guys are adopting AI
and automation in their cyber attacks. Government and other
institutions need to be using more AI in defense in order to,
as you said, Senator, find vulnerabilities before they're
exploited in order to be able to react at machine speed when
things start to go wrong, and in order to better understand
what are the possible implications of the way that systems are
set up so we don't get surprised in the way that institutions
nowadays too often are by both that a breach occurs and by how
bad the consequences are.
Dr. Gil. Yes, there are two dimensions are essential in
this topic. One of it actually has to do with securing AI
itself. The very models that we create to enable these
predictions are actually vulnerable to attack, and that in
itself, there are many steps that can be taken to be able to
secure those models. In fact, you can even extract data from a
model that has been created.
And the second I mention is the one you alluded to, which
is AI itself has to be an integral component to protect against
other AI-powered attacks. So in a way, we are going to have AI
against AI in the realm of cybersecurity because, you know,
some of the bad guys are already using these techniques to be
able to attack our networks with the speed and accuracy and
adaptability that without the presence of AI, and Ms. Espinel
alluded to this already, it will be impossible to defend
against.
Senator Cantwell. Did you want to comment on that as well?
Ms. Espinel. Just I'll just say briefly that IBM is among
the BSA members that are focused on this. We have a number of
companies that are very focused on cybersecurity and are using
AI to try to detect patterns and then predict threats before
they happen. So I agree with you, that I think it's an area
where AI is already being used, and there is potential for it
to be doing even more.
Senator Cantwell. Well, I like the notion just in the fact
that because there are things called human error, that you can,
you know, use this to look for faults in code and weak points
and do--because that's what someone else is doing, right? And
so the sooner that you can find that yourselves and detect that
and create another security layer, the better.
Is there any other government application that you think we
should be investigating on? Some people say on like health
statistics and things of that nature, but I don't know if you--
anybody else on the panel has----
Mr. Castro. We did look last week into government use of
AI, and one of the biggest challenges is that we're just not
measuring what we're doing within government, that there are
lots of opportunities, but there's not a good place where if
you're an agency, not even a CIO, but just a team manager
basically, who wants to start using, you know, automated
calendaring, you know, how do I actually go out and do that
quickly? You know, can I quickly procure this? Is there an
approved list of, you know, best practices from other agencies?
Is there good information sharing? We're starting to do that
through GSA, but we're not there yet.
So one of the things that this Committee could hopefully
help do is to really push agencies to ask--bring the CIOs in
here and ask them, ``What are you doing around AI? Have you
identified the top three opportunities and are you pursuing
them?'' Just like, you know, many agencies were directed to
find the high-value datasets, pick three, and get them out
there as open data, we can do something similar around AI, pick
the top three opportunity targets and pursue that over the next
12 months. Similarly----
Ms. Espinel. I would agree with that, but I think city
planning is another area where AI could be really helpful. So
like traffic congestion, you know, one of the things that city
planners are trying to do is optimize traffic patterns and the
changing of traffic lights to try to improve traffic flow, and
that's something that AI is really good at. You know, there are
a lot of variables, so traffic congestion can seem like a
relatively simple question, but if you think of all the
variables and traffic patterns, it's actually quite complicated
in taking all that data and then giving city planners
recommendations for how to optimize traffic flow is something
AI can do really well.
Senator Cantwell. Thank you.
Thank you, Mr. Chairman.
Senator Wicker. Dr. Bethel, you are doing practical
applications on high-risk law enforcement situations,
autonomous cargo transfer, and animal-assisted therapy. Do you
receive Federal research dollars for that? And to what extent?
Dr. Bethel. The law enforcement application we have
submitted to NSF. We have currently one grant under review. The
Therabot was funded under NSF funding. And some other projects
that we're doing are also funded through the NSF or Department
of Defense.
Senator Wicker. OK. Now, Senator Schatz mentioned a real
concern with regard to law enforcement. What you all are doing,
though, at Mississippi State, is scenarios where there is
already a threat, and law enforcement needs information about
how to respond, how to get inside the building. Is there a
child there? Is there something explosive there? Has anyone
raised the concerns that he raised, any of your law enforcement
people or victims or defendants, raised concerns about data
bias?
Dr. Bethel. Law enforcement has not mentioned any data bias
because when we are using these algorithms, we are looking at
more like objects in the scene----
Senator Wicker. Something is already occurring.
Dr. Bethel. Something is occurring. We're not trying to use
it to target people, we're using it because the scene--some
kind of event has occurred, and law enforcement is responding.
And so we are trying to provide them as much information as we
can prior to them going in so that hopefully they can make
better, more safer, decisions when they enter into the
environment. For instance, their protocol changes completely if
there's a child inside the home that they're going into. They
can't use a flash bang, they can't do things they would
normally do.
So we can, by sending a robot in to be able to see what's
happening prior, they can make decisions, so they're probably
going to end up saving lives, both civilian and law enforcement
lives, because--and they will have better performance because
of it. So bias hasn't so far come into our discussions when
we've been looking at law enforcement applications, but we are
not using it, I think, in the manner in which Senator Schatz
has indicated.
Senator Wicker. Now, with regard--first of all, on the law
enforcement application, how long has Mississippi State been
doing this?
Dr. Bethel. Six years. I started training with them in
2011. We train monthly.
Senator Wicker. OK. AI, according to your testimony, is
only as good as the data the system receives. How has your data
improved over the 6 years, and could you elaborate on that?
Dr. Bethel. So each training we do, we do video recordings,
we do sensor recordings, we do different manners of data
collection. The more examples of data that we can get and
obtain, the better the system is at classifying information,
doing the sensor fusion, and incorporating that information to
make more informed decisions. So as time has gone on, we've
been able to obtain more and more samples of data to be able to
use that for the systems.
Senator Wicker. OK. Now, most of the panel, if not all the
panel, seems to think we lead the world, it's just a question
of whether we're going to continue leading the world. Senator
Schatz said he doesn't think with regard to national policy
we'll take a European approach. Rather than ask him what he
means by that, let me ask the panel, are there mistakes that
our international competitors, whether in Europe or Asia or
Africa or wherever, are making that we need to avoid? Are there
overreaches in terms of regulation that we need to avoid?
Everybody is trying to get ahead. The testimony is that
China is making an important effort, the U.K., Japan, our
neighbor to the north, but in terms of things we can avoid,
mistakes that other countries have made, does anybody have a
suggestion that we need to be mindful of as we work on Senator
Young and Senator Cantwell's and Senator Schatz's legislation?
Yes, Mr. Castro.
Mr. Castro. Yes, so, I mean, the two big things I think
some countries are thinking about is the right to explanation
that's across the board or right to opt out of automated
decisions. So the problem with those two policies is that it
minimizes the ability or limits the ability of companies to
deploy AI and many commercial applications.
So, for example, if you want to be a company, you know,
just like before you had companies suddenly become online
lenders, if you want to be a company that's an AI lender,
streamline your entire business process through AI, you
basically can't do that because anyone who got rejected for a
loan application, for example, could ask for a human appeal,
and you would have to have human support to do that.
And the problem with that kind of framework is it basically
limits those types of business models, and it's especially
problematic when you talk about global competition because it
doesn't limit necessarily a foreign competitor from offering a
similar service. Now, in certain industries, of course, you
have to be licensed in the United States, so there are limits
there, but overall, that would limit these kind of
opportunities.
Ms. Espinel. Can I just say briefly? I think most other
governments are still considering what their AI policy
environment is going to look like. So many governments are
having this discussion right now. I do think that there are
governments that are--seem to be skewing toward considering a
more regulatory approach, a more heavy-handed regulatory
approach, and that raises concerns.
And I think one issue I'd raise in particular I think for
us, I think any regulatory approach that would compel the
disclosure of an algorithm, that would compel companies to hand
over source code or algorithms, to a government agency is one
that raises a lot of concerns with us. We don't think it's
actually going to be an effective way to address policy
outcomes, and it raises a lot of competitive concerns to be
handing over algorithms or source codes to governments.
Senator Wicker. Dr. Gil, on cyber attacks from hostile
forces internationally and our defense against that, do I
understand you to--well, no, let me rephrase it. Is it
conceivable that artificial intelligence will be empowered by a
hostile force to make a decision to go forward with a cyber
attack without a human being at that end making the decision to
pull the trigger?
Dr. Gil. Humans will definitely design AI-powered attacks
to attack, you know, infrastructure or, you know, an industrial
setting or another nation-state----
Senator Wicker. But whether that attack is made today or
Valentine's Day next year, an artificial intelligence----
Dr. Gil. System.
Senator Wicker.--system might make that decision.
Dr. Gil. That's correct. That is possible----
Senator Wicker. How close is that to reality?
Dr. Gil.--because, I mean, think about it in the past for--
you could design it with an explicit program model to be able
to carry out such attacks, and we would have to, you know,
create defense mechanisms against those kinds of attacks. But
because the program was well stipulated, you could imagine
somebody defending against the attack to be able to interpret
what those rules of attack were.
Now, the moment that you're employing a more machine-
learning-based approach where the type of attack could morph
depending on the environment it's detecting, now being able to
detect what is the form of the attack that is taking place
requires another pattern detection mechanism. So that's why I
was referring to--and Ms. Espinel was talking before--about you
need AI to defend against AI-powered attacks because it's the
only way to make it really adaptive to an adaptive attack.
Senator Wicker. OK. Senator Markey, do you mind if we--if
we let the two witnesses respond here? And then I'll be
generous with your time.
Dr. Felten?
Dr. Felten. Thank you. What you're talking about in terms
of automated cyber attack is something we see already with
computer viruses. A virus is software which autonomously
spreads itself from place to place, and then does whatever it's
programmed to do to cause harm at the places that it infects.
So to bring AI into this would just be to have a more
sophisticated, more adaptive form of virus. It's not
fundamentally a new thing, it is--it's a path that we are
already on, or a path that the bad guys are already on.
Senator Wicker. And quickly, Ms. Espinel.
Ms. Espinel. I still think it's more akin to making a
recommendation than making a decision. Of course, you, the
person, can decide that whatever recommendation the system
gives you, you're going to have it automatically act on, but
you still made that decision up front, and I don't think we
should be--as a general matter, I don't think we should be
abrogating decisionmaking authority, but I think that what
you're talking about really is AI making a recommendation, and
then the person who designed it deciding whether or not they're
going to accept that recommendation automatically or not.
Senator Wicker. We're really talking about what's coming at
us.
Senator Markey.
STATEMENT OF HON. EDWARD MARKEY,
U.S. SENATOR FROM MASSACHUSETTS
Senator Markey. Thank you, Mr. Chairman, very much.
I thank you all for being here.
The digital revolution's newest innovations--augmented
reality, autonomous vehicles, drones--all of these industries
use artificial intelligence, which rely heavily on a free and
open Internet. Regrettably, these disrupting technologies may
be dealt a major blow in their infancy, and that's because in
just 2 days, the FCC will vote on a proposal to eliminate net
neutrality.
Without enforceable net neutrality rules in place,
broadband providers, like Comcast, Verizon, AT&T, and Spectrum,
could block or slow down the content of innovators and
businesses using AI all in an effort to leverage their
gatekeeper role to favor their own content and generate
additional profits. And what will replace these robust net
neutrality protections? Nothing, absolutely nothing is going to
replace those rules.
Dr. Felten, how would eliminating net neutrality
protections impact the deployment and development of innovative
technologies that use AI?
Dr. Felten. A lot of AI technologies operate within an
institution or within a company's data center, and so those
technologies would not be much affected by changes in network
policy. To the extent that changes in network policy affect the
ability of companies to deliver their products to consumers,
that would obviously be a policy concern, but in my mind, it is
important but also somewhat separate from the issue of
development of AI.
Senator Markey. So you don't think that the additional cost
to the developer, to the innovator, won't have an inhibiting
impact upon their ability to go to the capital markets, raise
the dough, in order to produce their innovation knowing that
there's no guarantee that they can reach the 320 million
Americans without paid prioritization or without a threat of
throttling or blocking?
Dr. Felten. I would say that all pro-innovation policies
and all policies that are designed to help small companies
enter and compete provide value and are pro-innovation and
valuable. As I said, I don't think that net neutrality plays a
special role with respect to AI.
Senator Markey. But does it----
Dr. Felten. There are other areas of innovation.
Senator Markey. Will it play a role in your opinion?
Dr. Felten. Yes. I think those decisions do play a role in
almost any area of innovation.
Senator Markey. OK. And on the question of child privacy,
the Child Online Privacy Protection Act of 1998 is still the
communications constitution for safeguarding children online,
and that's a law I was able to get passed back in 1998.
As emerging technologies like AI are deployed, it's
important that they honor core American values, including
privacy. Dr. Felten, could AI technologies pose a threat to
children's privacy? And is there a threat that AI technologies
could produce inappropriate content for children?
Dr. Felten. This is an issue to pay attention to. AI does
raise the stakes on policy--I'm sorry--AI raises the stakes on
privacy discussions generally, and that's true with respect to
children and others. And, of course, parents are very concerned
about what their kids see and what happens, and that's one of
the reasons why COPA, for example, requires parental consent
before certain uses of data are allowed.
Senator Markey. So could relying on AI in children's toys
negatively impact kids' ability to develop empathy if we
substitute real people with computers that cannot fully
understand emotion as humans do, Dr. Felten?
Dr. Felten. Well, I think kids are more interested in
playing with other kids or using toys as a vehicle for playing
with other kids. I'm less worried about kids bonding with
something that is not human-like or not companionable. I think
kids will reject those on their own.
Senator Markey. Yes. Earlier this year I wrote to Mattel
with serious concerns about their plan to bring the first all-
in-one voice-controlled smart baby monitor to the market.
Mattel had planned for the device, Aristotle, to use artificial
intelligence to help teach children and respond to their needs.
After an outcry of questions, Mattel canceled that product.
Dr. Felten, what does that experience expose as to
potential negatives of using artificial intelligence with
children's devices?
Dr. Felten. Well, I think stories like this illustrate that
it's important to understand the implications of the
technologies that are being deployed. As I would imagine, that
parents would be happy to, say, be notified if there is
indication that their child is in distress, and AI may help to
do that more effectively. But these issues of unintended
consequences and safety are paramount, and that's one of the
important aspects of clearing the road for responsible
deployment of AI, is to make sure these issues are taken care
of.
Senator Markey. Thank you.
Senator Wicker. Senator Cruz.
STATEMENT OF HON. TED CRUZ,
U.S. SENATOR FROM TEXAS
Senator Cruz. Thank you, Mr. Chairman.
Thank you to each of the witnesses for coming here this
morning to testify.
A little over a year ago, the Subcommittee on Science and
Space, which I chair, held the first congressional hearing on
artificial intelligence. And then, as now, we heard testimony
about the extraordinary transformative process that we are
engaged in right now and how AI in time can be expected to
touch virtually every area of human endeavor, and indeed that
this transformation may be of comparable import to the
transformation we engaged in, in a prior era in the Industrial
Age.
Anytime we're seeing dramatic transformations in our
economy and our workforce and how we interact with each other,
that poses the risk of dislocations, but it also poses policy
and government and regulatory challenges for how to interact
with the new terrain. In your judgment, what are the biggest
barriers right now to developing and expanding AI and its
positive impacts on our society and our economy?
Ms. Espinel. I'll head off another--so I think one of the
biggest barriers is a lack of understanding, a lack of
understanding about what AI is, what the actual technology is,
and then what it does and what the--you know, what both the
intended and unintended consequences are. And so I think, you
know, this hearing, the legislation that Senator Schatz is
working on, I think trying to increase our collective
understanding is critical, it's fundamental.
I think there are a number of specific policy issues that
would be helpful in terms of eliminating barriers. So, you
know, one of those is AI is all about data, and so good data
policy in various ways I think is very important. I think
investing in research, which we've talked about already,
investing both in government research and incentivizing private
sector research, is very important. And then I think thinking
about jobs and workforce development, both the jobs today, but
what will happen tomorrow? And rethinking our educational
system and our training and reskilling programs are vitally
important. So those are the three specific areas, but I think a
greater understanding needs to be part of all of those
discussions.
Mr. Castro. So I agree that skills are very important.
Especially much of this technology is new, and we need people
that can basically rapidly be credentialed in how to deploy it.
But when we look at the biggest opportunities for AI, it's
really in some of the regulated industries. And so I think
that's where we head up on challenges because there are two
challenges: one, regulators aren't necessarily prepared with
how to deal with this; and, two, they don't necessarily have
the skill set or capabilities internally within the regulatory
system to handle it. And so I think that's something we need to
be very focused on, is asking questions----
Senator Cruz. What agencies in the industries in
particular?
Mr. Castro. So financial regulation, for example. Education
is another example, especially when we're talking about using
AI in primary education, there are a lot of questions about
privacy that get raised. And the financial system it's the
questions of, you know, do we have regulation basically to
understand the technology? And we'll do things more than just
ask, ``Can I see the algorithm?'' but be able to say, ``Can I
look at outcomes? Can I actually measure outcomes?'' and then
ask questions about, ``OK, is this fair? Is this different than
what we have now? Is this moving in the right direction?'' and
also have some of that regulatory flexibility. So we need kind
of, you know, fewer cops and more sandboxes.
Senator Cruz. So one area that has generated fears and
concern is general AI, and scientists and innovators ranging
from Stephen Hawking to Bill Gates to Elon Musk have raised
concerns. Stephen Hawking stated, quote, ``Once humans develop
artificial intelligence, it would take off on its own and
redesign itself at an ever-increasing rate. Humans, who are
limited by slow biological evolution, couldn't compete and
would be superseded.'' Elon Musk has referred to it as, quote,
``Summoning the demon.''
How concerned should we be about the prospect of general
AI? Or to ask the question differently, in a nod to Terminator,
when does Skynet go online?
[Laughter.]
Dr. Felten. Hopefully never.
Senator Cruz. That's the right answer.
Dr. Felten. I think there's a lot of debate within the
technical community about how likely these sorts of scenarios
might be. I think virtually everyone agrees that it would be
far in the future. And generally the people who are most
involved in AI research and development tend to be the most
skeptical about the Skynet or existential risk type of
scenarios.
In any case, the sorts of risks and concerns that exist now
about AI are really baby versions of the ones that we would
face with a more sophisticated general AI. And so the tactics
we--the policies that make sense now to deal with the issues we
face now are the same ones we would use to warm up for a
general AI future if it comes. And so from a policy choice
standpoint, the possibility of distant general AI seems less
important to me.
Dr. Gil. Yes, I would completely agree. I think if you ask
practitioners in the field when they would envision the
possibility of that, I think everybody would say 20-plus years
out. And whenever scientists says 20-plus years out is our code
word for saying we just don't know. Right? We're nowhere near
close to be able to do that.
So I do think that while it's an area of very important
study, and there are many universities who are now creating a
rate of monitoring of what is the progress of AI and the
implications that it would have if we eventually reach general
artificial intelligence, I do think it would be a mistake to
guide our policy decisions at present based on the sort of like
long-term hypothetical that we don't even have, even as
practitioners, even a credible path to get there.
Senator Cruz. Thank you.
Senator Wicker. Senator Cortez Masto.
STATEMENT OF HON. CATHERINE CORTEZ MASTO,
U.S. SENATOR FROM NEVADA
Senator Cortez Masto. Thank you, Mr. Chairman.
And thank you to the panel members. I'm very excited about
the conversation today as well.
Obviously, we are standing at the edge of a technological
revolution that we must ensure will, and there has been
discussion, take our labor force with it. So this is a timely
conversation.
Workforce development has been a focus of mine since I've
entered the Senate, and so has innovation. I've proudly been
leading a bipartisan legislation on drone expansion, the use of
smart technology in transportation, and in trying to spur the
next generation of women to be equal at the forefront of STEM,
specifically computer programming, through the introduction of
the Code Like a Girl Act. I've worked on this because I've seen
the future of these developments through my state in Nevada.
And just last week I was visited by the leadership at the
Truckee Meadows Community College in Reno, who, in partnership
with Panasonic, has developed a curriculum to provide
individuals to get the specific training that local jobs are
hiring for. These are conversations that constantly are going
on in my state.
So let me start here, Mr. Felten and Ms. Espinel. The
skills gap has been discussed extensively today, and something
that obviously is on all of our minds and how we address it.
Obviously, we have China investing dramatically in the area of
AI, so it begs the question of whether there are investments we
can be making at the Federal level to help close any potential
skills gaps. I'm curious your thoughts on that.
Ms. Espinel. So in terms of the skills gap specifically, we
also talked a little bit about Federal support for research
funding. But I would say a few, and then others may add to
them.
I think so one is trying to improve access to computer
science education at all states and at very early stages of
education. So I think that's one area where the Federal
Government could be helpful.
I think the second is in rethinking how our vocational
training programs work. There are vocational training programs
in place, but they could be streamlined and they could be
better adapted to the world that we live in today. So I think
that's an area where there's a lot that could done at the
Federal level.
And then the third I would say is that I think there is
now, and there will maybe be only an increasing sort of
information gap between the skills that people have and
employers knowing about those, and that seems like an area
where there's a real deficiency now, so therefore real
opportunity to try to create programs that not only create--
either create pathways directly into employment or do a better
job of matching up skills that people have with the jobs that
employers have to offer.
Senator Cortez Masto. Thank you.
Dr. Felten. There are opportunities to widen the pipeline
at all stages starting with K-12, making sure that basic
computer science education is available to every child at that
level, and more advanced education at the high school level for
those who are ready for it.
There are opportunities to improve--increase the number of
teachers and trainee researchers at the university level, and
that's education and research funding specifically in areas of
AI and computer science at the university level. And then
vocational and adult training and apprenticeship programs also
are very important to get people on other career paths, give
them an on-ramp into this area.
Senator Cortez Masto. Go ahead.
Ms. Espinel. Can I just say one more thing?
Senator Cortez Masto. Yes.
Ms. Espinel. There's a lot that BSA companies are doing in
this area, IBM among them. And so I think another area is
working with the Federal Government both to scale up programs
that are effective now and be more collaborative in this area I
think is----
Dr. Gil. Just to touch on just one example of that. On the
initiative, on a program, we started a number of years called
P-TECH, which is a 9 through 14 educational program that
combines both education, hands-on program on vocational, you
know, schools. And when they graduate through this--right?--we
are talking about the creations of new collar professions that
have enough skills to be able to practice and benefit from the
advances that are happening in AI without having to go through
a full college education, and now that's touching, you know,
more than 10,000 students.
Senator Cortez Masto. Thank you. No, this is something
that's happening in Nevada now. It seems like it's such common
sense, but we just don't do it, which is that collaboration and
partnership between whether it's a government, private sector,
and then our education system or our skills vocational trade,
to really say, ``What are the jobs of the future? What are they
going to look like?'' work with those employers to develop the
curriculum that you're going to need for that skilled workforce
so that we can really start educating them now. And that starts
I think at a very young age and working with our education
system, but also the vocation trades that are out there as
well. Not every child is going to go on to get a higher degree,
but there are some that are absolutely and rightfully are going
to go get that vocation or that trade and that skill that's
necessary.
So I appreciate the comments today. I am running out of
time, so I will submit the rest of my questions to this
incredible panel for the record. And I appreciate you being
here. Thank you.
Senator Wicker. Thank you very much.
Senator Blumenthal.
STATEMENT OF HON. RICHARD BLUMENTHAL,
U.S. SENATOR FROM CONNECTICUT
Senator Blumenthal. Thanks, Mr. Chairman. I apologize that
I missed some of your testimony so far, but I know that this
panel has been very important and enlightening, and I thank you
for being here.
As you well know, the success of autonomous vehicles is
closely linked with the success of AI. An autonomous vehicle
can only perform as well as the input it receives. A lot of us
know the statistic that 94 percent of the 37,000 deaths on the
road each year are attributable to human error--97 percent. The
hope is that autonomous vehicles will eliminate that human
error.
What we really lack is information on the extent, to what
extent that is caused by human error will be possibly replaced
by computer error. And we know computers are not infallible.
I wonder, Dr. Bethel, whether you could talk about some of
the tasks in which humans still perform better than computers
or an AI system in the context of autonomous vehicles, if
you're able.
Dr. Bethel. In the context of autonomous vehicles, humans
are able to adjust to very rapidly changing, unpredictable
environments, things happening in the environment. Our sensor
systems and our onboard processing in autonomous vehicles is
just not to a point where it can make those kind of adjustments
that rapidly currently with current technology to be able to
adjust to that. So in those cases where you have an erratic
driver who is doing unpredictable behaviors, it's really hard
sometimes for the system to be able to detect that and react
accordingly. In those cases, a human currently is making better
decisions on autonomy--over an autonomous vehicle in that kind
of environment.
Senator Blumenthal. So if I can just extrapolate from your
answer, a computer trying to deal with a drunk driver, either
ahead or next to that computer, would have trouble because the
drunk driver, by definition, is acting in not only
unpredictable, but irrational and sometimes actually self-
destructive ways.
Dr. Bethel. Right. So it would be much more difficult for a
computer to predict that kind of behavior than it would be for
a human to slow down and react. I mean, it would react, but it
probably is not going to be as effective as a human driver is
at this point in the stage of where AI is.
Senator Blumenthal. Are there ways to program a computer or
create software that deals with those unpredictable situations?
Dr. Bethel. To some extent, but I think there's a long way
to go on that.
Senator Blumenthal. In your testimony, you say, and I'm
quoting, ``A current limitation to the advancement of
artificial intelligence is the quality and cost effectiveness
of sensing capabilities to provide high-quality information or
data to the system to make those digital decisions. We have a
long way to go in the advancement of artificial intelligence--
,'' ``We have come a long way in the advancement of artificial
intelligence,'' excuse me, ``however, we still have a long way
to go.''
Sensing, perceiving, interpreting, surroundings are
essential to driving a vehicle. Can you describe some of the
limitations in the current computer vision and sensing
technologies?
Dr. Bethel. I'll probably need to get back to you on some
of that because it's not exactly my area of expertise related
to computer vision, but there are limitations in the sensing
capabilities we currently have. Every sensor has its own
limitations, so there's no perfect sensor out there.
There are also differences in processing power, so trying
to be able to handle large amounts of data coming in from these
sensors, and processing that in a timely manner can be a
challenge. So that's another area. And computer vision has just
mega amounts of data coming in that has to be processed, and so
another limitation is the actual processing power to handle
that, especially in a timely real-time manner onboard a system.
Senator Blumenthal. At what point do you think it would be
appropriate to completely remove the human from an autonomous
vehicle?
Dr. Bethel. Thank you for the question. I--depending on the
application, I think there are applications currently where
autonomy could be--fully autonomous systems are capable. I
think it's not realistic anytime in the near future, and
especially in autonomous cars, to be able to say that a fully
autonomous car is going to be possible all the time.
Senator Blumenthal. Thank you.
Thank you very much to the panel.
Thank you, Mr. Chairman.
Senator Wicker. Senator Schatz.
Senator Schatz. Dr. Felten, I just wanted to follow up on
the question around autonomous weapons. It seems to me that
this is an area that is different than a lot of these other
ethical, societal, micro, macro, economic questions; this is
about how we engage in warfighting. And so to the degree and
extent that some of these algorithms are hackable, to the
degree and extent that we have a body of history around how
we're supposed to engage our military in an ethical manner,
that this is an area where the Federal Government has to make
policy.
And I'm wondering if you can give us any insight into, in
the absence of policymaking, who's making these decisions? Is
it defense contractors? Is it individual procurement officers?
How is all of this getting decided?
Dr. Felten. Well, there does need to be a policy which
deals with the very serious issues that you mentioned, both
from the standpoint of what our military should be willing to
do and what safeguards are needed on the systems that they're
using consistent with the need for them to be as effective as
possible in battle, and also how we deal with adversaries, who
might not be as scrupulous in following the international
humanitarian law.
And this is a national policy issue that ought to be a
matter of policy discussion at the highest levels. And if it's
done in a decentralized way, if each contractor, each
contracting officer, does one thing, if the State Department
goes to international arms control discussions and does their
own thing, we get uncoordinated policy and we get a result that
doesn't serve the American people well.
Senator Schatz. Thank you.
Senator Wicker. Well, I would like to thank everyone who
has participated. And I think Senator Cortez Masto said this
has been an incredible panel, and I would have to agree.
Ms. Espinel, you brought information about software and the
way a number of states have benefited. Did you have information
for all 50 states or just for the states represented by this
panel?
Ms. Espinel. We have it for all 50 states. Our foundation
software data put out a study in September for each of the 50
states.
Senator Wicker. OK. Well, then if you would, please enter
that into the record.
Ms. Espinel. I'd be delighted to.
[The information referred to follows:]
[GRAPHIC(S) NOT AVAILABLE IN TIFF FORMAT]
Senator Wicker. I would like to enter into the record a
letter from the Information Technology Industry Council and a
letter from the Electronic Privacy Information Center without
objection. And that's so ordered.
[The information referred to follows:]
Information Technology Industry Council
Washington, DC, December 11, 2017
Hon. Roger Wicker, Chairman,
Hon. Brian Schatz, Ranking Member,
Subcommittee on Communications, Technology, Innovation, and the
Internet,
U.S. Senate Committee on Commerce, Science, and Transportation,
Washington, DC.
Dear Chairman Wicker and Ranking Member Schatz:
In advance of your hearing on ``Digital Decision-Making: The
Building Blocks of Machine Learning and Artificial Intelligence,'' I am
writing to thank you for interest in and attention to the exciting
innovation that is Artificial Intelligence (AI). ITI represents more
than 60 of the world's leading information and communications
technology (ICT) companies. Our companies are the most dynamic and
innovative companies from all corners of the ICT sector, including the
development and deployment of AI. I submit this letter on behalf of ITI
and its members, and respectfully request that you enter it into the
hearing record.
Artificial intelligence (AI) technology is an integral part of our
daily lives, work, and existence. It's already made an important mark
on much of our society and economy, and the exciting part is that we're
just seeing the beginning of its benefits.
Go to any hospital and medical research center and you will see how
doctors and medical providers use AI to save lives. For example, the
company Berg uses AI to analyze large amounts of oncological data to
create a model of how pancreatic cancer functions, enabling us to
develop chemotherapy to which cancer cells are more responsive.
Educators across the country use AI to enhance the potential for
future generations to grow and learn. Thanks to IBM's Teacher Advisor,
a new tool based on its Watson cognitive computing platform, third-
grade math teachers can develop personalized lesson plans. Teacher
Advisor analyzes education standards, sets targets for skills
development, and uses student data to help teachers tailor
instructional material for students with varying skill levels.
AI also makes day-to-day of life easier--for everyone. Many of our
everyday tasks, like making shopping lists and ordering groceries, are
streamlined through devices like Alexa. And, through AI technology,
researchers at the International Islamic University of Malaysia have
developed the Automatic Sign Language Translator (ASLT) that uses
machine learning to interpret sign language and convert it into text,
easing communications for many.
We know AI will revolutionize the way we do business and our
overall economy. It's projected AI will add between $7.1 trillion and
$13.17 trillion to the global economy by 2025.\1\
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\1\ https://www.itic.org/resources/AI-Policy-Principles-
FullReport2.pdf
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There's no question AI will continue to transform our lives,
society, and economy for the better. We understand, however, that there
are many questions about this technology and that with transformative
innovation, there are going to be points of tension. The tech industry
is committed to working with all stakeholders to identify and resolve
challenges.
In conjunction with the global leaders in AI innovation, ITI
recently published AI Policy Principles. These principles are designed
to be guidelines for the responsible development and deployment of AI
as we develop partnerships with governments, academia, and the public.
Our Policy Principles are a conversation catalyst, encouraging all
stakeholders, public and private, to collaborate to create smart
policies that allow this emerging technology to flourish while
addressing the complex issues that arise out of its growth and
deployment. Given the reach of AI, we think this kind of partnership
and engagement is critical to advance the benefits and responsible
growth of AI while also endeavoring to answer the public's questions
about the use of this nascent technology.
We look forward to working with this Committee, other members of
Congress, academia, industry partners, and the public to advance AI
responsibly. Thank you, again, for holding the timely and important
hearing.
Dean Garfield,
President and CEO,
Information Technology Industry Council (ITI)
______
Electronic Privacy Information Center
Washington, DC, December 12, 2017
Hon. John Thune, Chairman,
Hon. Bill Nelson, Ranking Member,
U.S. Senate Committee on Commerce, Science, and Transportation,
Washington, DC.
Dear Chairman Thune and Ranking Member Nelson:
We write to you regarding the ``Digital Decision-Making: The
Building Blocks of Machine Learning and Artificial Intelligence''
hearing.\1\ EPIC is a public interest research center established in
1994 to focus public attention on emerging privacy and civil liberties
issues.\2\ EPIC has promoted ``Algorithmic Transparency'' for many
years.\3\
---------------------------------------------------------------------------
\1\ Digital Decision-Making: The Building Blocks of Machine
Learning and Artificial Intelligence, 115th Cong. (2017), S. Comm. on
Commerce, Science, and Transportation, https://www.commerce.senate.gov/
public/index.cfm/hearings?ID=7097E2B0-4A6B-4D92-85C3-D48E100
8C8FD (Dec. 12, 2017).
\2\ EPIC, About EPIC, https://epic.org/epic/about.html.
\3\ EPIC, Algorithmic Transparency, https://epic.org/algorithmic-
transparency/.
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Democratic governance is built on principles of procedural fairness
and transparency. And accountability is key to decision making. We must
know the basis of decisions, whether right or wrong. But as decisions
are automated, and organizations increasingly delegate decisionmaking
to techniques they do not fully understand, processes become more
opaque and less accountable. It is therefore imperative that
algorithmic process be open, provable, and accountable. Arguments that
algorithmic transparency is impossible or ``too complex'' are not
reassuring.
It is becoming increasingly clear that Congress must regulate AI to
ensure accountability and transparency:
Algorithms are often used to make adverse decisions about
people. Algorithms deny people educational opportunities,
employment, housing, insurance, and credit.\4\ Many of these
decisions are entirely opaque, leaving individuals to wonder
whether the decisions were accurate, fair, or even about them.
---------------------------------------------------------------------------
\4\ Danielle Keats Citron & Frank Pasquale, The Scored Society: Due
Process for Automated Predictions, 89 Wash. L. Rev. 1 (2014).
Secret algorithms are deployed in the criminal justice
system to assess forensic evidence, determine sentences, to
even decide guilt or innocence.\5\ Several states use
proprietary commercial systems, not subject to open government
laws, to determine guilt or innocence. The Model Penal Code
recommends the implementation of recidivism-based actuarial
instruments in sentencing guidelines.\6\ But these systems,
which defendants have no way to challenge are racially biased,
unaccountable, and unreliable for forecasting violent crime.\7\
---------------------------------------------------------------------------
\5\ EPIC v. DOJ (Criminal Justice Algorithms), EPIC, https://
epic.org/foia/doj/criminal-justice-algorithms/; Algorithms in the
Criminal Justice System, EPIC, https://epic.org/algorithmic-
transparency/crim-justice/.
\6\ Model Penal Code: Sentencing Sec. 6B.09 (Am. Law. Inst.,
Tentative Draft No. 2, 2011).
\7\ Julia Angwin et al., Machine Bias, ProPublica (May 23, 2016),
https://www.propublica.org/article/machine-bias-risk-assessments-in-
criminal-sentencing.
Algorithms are used for social control. China's Communist
Party is deploying a ``social credit'' system that assigns to
each person government-determined favorability rating.
``Infractions such as fare cheating, jaywalking, and violating
family-planning rules'' would affect a person's rating.\8\ Low
ratings are also assigned to those who frequent disfavored
websites or socialize with others who have low ratings.
Citizens with low ratings will have trouble getting loans or
government services. Citizens with high rating, assigned by the
government, receive preferential treatment across a wide range
of programs and activities.
---------------------------------------------------------------------------
\8\ Josh Chin & Gillian Wong, China's New Tool for Social Control:
A Credit Rating for Everything, Wall Street J., Nov. 28, 2016, http://
www.wsj.com/articles/chinas-new-tool-for-social-control-a-credit-
rating-for-everything-1480351590
In the United States, U.S. Customs and Border Protection has
used secret analytic tools to assign ``risk assessments'' to
U.S. travelers.\9\ These risk assessments, assigned by the U.S.
Government to U.S. citizens, raise fundamental questions about
government accountability, due process, and fairness. They may
also be taking us closer to the Chinese system of social
control through AI.
---------------------------------------------------------------------------
\9\ EPIC v. CBP (Analytical Framework for Intelligence), EPIC,
https://epic.org/foia/dhs/cbp/afi/.
In a recent consumer complaint to the Federal Trade Commission,
EPIC challenged the secret scoring of young athletes.\10\ As EPIC's
complaint regarding the Universal Tennis Rating system makes clear, the
``UTR score defines the status of young athletes in all tennis related
activity; impacts opportunities for scholarship, education and
employment; and may in the future provide the basis for `social
scoring' and government rating of citizens.'' \11\ As we explained to
the FTC, ``EPIC seeks to ensure that all rating systems concerning
individuals are open, transparent and accountable.'' \12\
---------------------------------------------------------------------------
\10\ EPIC, EPIC Asks FTC to Stop System for Secret Scoring of Young
Athletes (May 17, 2017), https://epic.org/2017/05/epic-asks-ftc-to-
stop-system-f.html; See also Shanya Possess, Privacy Group Challenges
Secret Tennis Scoring System, Law360, May 17, 2017, https://www
.law360.com/articles/925379; Lexology, EPIC Takes a Swing at Youth
Tennis Ratings, June 1, 2017, https://www.lexology.com/library/
detail.aspx?g=604e3321-dfc8-4f46-9afc-abd47c5a5179
\11\ EPIC Complaint to Federal Trade Commission, In re Universal
Tennis at 1 (May 17, 2017).
\12\ Id.
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In re Universal Tennis, EPIC urged the FTC to (1) Initiate an
investigation of the collection, use, and disclosure of children's
personal information by Universal Tennis; (2) Halt Universal Tennis's
scoring of children without parental consent; (3) Require that
Universal Tennis make public the algorithm and other techniques that
produce the UTR; (4) Require that Universal Tennis establish formal
procedures for rectification of inaccurate, incomplete, and outdated
scoring procedures; and (5) Provide such other relief as the Commission
finds necessary and appropriate.\13\
---------------------------------------------------------------------------
\13\ Id. at 13.
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``Algorithmic Transparency'' must be a fundamental principle for
consumer protection The phrase has both literal and figurative
dimensions. In the literal sense, it is often necessary to determine
the precise factors that contribute to a decision. If, for example, a
government agency or private company considers a factor such as race,
gender, or religion to produce an adverse decision, then the decision-
making process should be subject to scrutiny and the relevant factors
identified.
On October 12, 2016, The White House announced two reports on the
impact of Artificial Intelligence on the U.S. economy and related
policy concerns. Preparing for the Future of Artificial Intelligence
concluded that ``practitioners must ensure that AI-enabled systems are
governable; that they are open, transparent, and understandable; that
they can work effectively with people; and that their operation will
remain consistent with human values and aspirations.'' \14\
---------------------------------------------------------------------------
\14\ Preparing for the Future of Artificial Intelligence, (Oct
2016), Executive Office of the President, National Science and
Technology Council, Comm. on Technology, https://obama
whitehouse.archives.gov/sites/default/files/whitehouse_files/
microsites/ostp/NSTC/preparing_
for_the_future_of_ai.pdf.
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Some have argued that algorithmic transparency is simply
impossible, given the complexity and fluidity of modern processes. But
if that is true, there must be some way to recapture the purpose of
transparency without simply relying on testing inputs and outputs. We
have seen recently that it is almost trivial to design programs that
evade testing.\15\ And central to the science and innovation is the
provability of results.
---------------------------------------------------------------------------
\15\ Jack Ewing, In '06 Slide Show, a Lesson in How VW Could Cheat,
N.Y. Times, Apr. 27, 2016, at A1.
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Europeans have long had a right to access ``the logic of the
processing'' concerning their personal information.\16\ That principle
is reflected in the U.S. in the publication of the FICO score, which
for many years remained a black box for consumers, establishing credit
worthiness without providing any information about the basis of
score.\17\
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\16\ Directive 95/46/EC--The Data Protection Directive, art 15 (1),
1995, http://www.data
protection.ie/docs/EU-Directive-95-46-EC--Chapter-2/93.htm.
\17\ Hadley Malcom, Banks Compete on Free Credit Score Offers, USA
Today, Jan. 25, 2015, http://www.usatoday.com/story/money/2015/01/25/
banks-free-credit-scores/22011803/.
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The continued deployment of AI-based systems raises profound issues
for democratic countries. As Professor Frank Pasquale has said:
Black box services are often wondrous to behold, but our black
box society has become dangerously unstable, unfair, and
unproductive. Neither New York quants nor California engineers
can deliver a sound economy or a secure society. Those are the
tasks of a citizenry, which can perform its job only as well as
it understands the stakes.\18\
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\18\ Frank Pasquale, The Black Box Society: The Secret Algorithms
that Control Money and Information 218 (Harvard University Press 2015).
We ask that this Statement from EPIC be entered in the hearing
record. We look forward to working with you on these issues of vital
importance to the American public.
Sincerely,
Marc Rotenberg,
President,
EPIC.
Caitriona Fitzgerald,
Policy Director,
EPIC.
Christine Bannan,
Policy Fellow.
EPIC.
Senator Wicker. The hearing record will remain open for 2
weeks. During this time, Senators are asked to submit any
questions for the record. Upon receipt, the witnesses are
requested to submit their written answers to the Committee as
soon as possible.
Thank you very much. The hearing is now adjourned.
[Whereupon, at 12 p.m., the hearing was adjourned.]
A P P E N D I X
Response to Written Question Submitted by Hon. Amy Klobuchar to
Dr. Cindy L. Bethel
Question. Political ads on the Internet are more popular now than
ever. In 2016, more than $1.4 billion was spent on digital
advertisements and experts project that number will continue to
increase. In October, I introduced the Honest Ads Act with Senators
Warner and McCain, to help prevent foreign interference in future
elections and improve the transparency of online political
advertisements. We know that 90 percent of the ads that Russia
purchased were issue ads meant to mislead and divide Americans.
Increasing transparency and accountability online will benefit
consumers and help safeguard future elections.
Dr. Bethel, could making more data about political advertisements
publicly available help improve the performance of algorithms designed
to prevent foreign interference?
Answer. More data that is high quality and has varied examples of
content may be helpful in improving AI algorithms in general. The data
though needs to have features that are detectable to learn from as part
of the machine learning process. It is not clear from the political
advertisements that have been promoted, as in the previous election, it
was not evident what features could be used to learn from to detect
involvement by foreign parties of these political advertisements. More
data does not always equate to better results. There needs to be
sufficient variations in key features that can be detected to be able
to develop and test algorithms that will be effective and will have
beneficial and meaningful results.
______
Response to Written Question Submitted by Hon. Tom Udall to
Dr. Cindy L. Bethel
Question. Can you speak of some of the ways that government funded
artificial intelligence development is now being used in the private
sector?
Answer. AI research has been funded for many years through the
National Science Foundation, National Institutes of Health, the
Department of Defense, USDA, among other agencies. It is possible that
there has been government-funded research into artificial intelligence
that has moved directly into the private sector for inclusion in
product development. Generally, concepts, algorithms, and advancements
developed as part of artificial intelligence research, has been
integrated into product developments in the private sector but it is
typically not a direct transfer from research into the private sector.
Currently, the research I am performing with Therabot
TM, the robotic therapeutic support companion, funded by the
National Science Foundation, developed algorithms associated with
artificial intelligence and machine learning that are being used in
this application. This is a project that is planned for
commercialization and making it available to the public. Mississippi
State University has received government funding for projects that
include artificial intelligence and have leveraged and transitioned
some of the that knowledge into industry-based projects that benefit
the private sector.
The private sector has been active in funding their own
advancements of artificial intelligence and machine learning. Many of
the developments and advancements in applications of AI have been
developed from private sector research and development groups. There
are also cases, where a researcher receives government-funded grants
for the development of AI and machine learning, and then may later
transition into a private sector position and takes that knowledge with
him or her to advance product development that benefits the private
sector.
There are numerous research developments that have been government
funded under SBIR/STTR mechanisms that are joint funding for industry
and academic researchers. NIH and USDA have been very active in
transitioning the technology developments using their funds into the
private sector and commercially available products. NSF and DoD also
has programs such as Innovation Corps (I-Corps) that transition
developments into commercialized and publicly available products. These
have been successful programs available to researchers who have been
funded under government grants and contracts.
I am not sure of all of the government-funded research to date in
artificial intelligence, so I am not sure exactly which projects have
benefited or ended up being applied in the private sector. That would
be a research project in itself that may be a worthwhile effort.
______
Response to Written Questions Submitted by Hon. Maggie Hassan to
Dr. Cindy L. Bethel
Question 1. Artificial intelligence, or AI, holds tremendous
promise for individuals who experience disabilities. For example,
Google and Microsoft have technologies to process language and speech
and translate it into a text format to assist individuals who are deaf
and hard of hearing. Other technologies will go even further to improve
the lives of people with disabilities and I would like to learn more
from the panel about what we can expect. What other specific
technologies are you aware of in the AI space that will help people who
experience disabilities?
Answer. There are numerous technologies that use artificial
intelligence that are being developed to assist people with different
types of disabilities to improve their quality of life. There is the
development and use of brain-machine interfaces for operating
wheelchairs and other devices through detecting signals sent with
intention from the brain. There are exoskeletons that are being
developed and prosthetics that learn and detect the signals and
impulses from the nervous system to be able to customize how these
devices work and enhance the capabilities of the end users. There are
in-home assistive robots that are being developed to assist disabled
and elderly people in their homes with reminders to take medications,
to fetch different items, and to remotely monitor users so they can
remain in their homes longer. The Therabot TM robot is being
developed as a home therapy support tool to provide comfort and support
for people who have experience post-traumatic stress or other types of
mental health disorders that can be used to detect and alert clinicians
or others when problems are detected. There are continual enhancements
to technologies for the hearing and visually impaired users. These are
just some of the many examples that use artificial intelligence to
improve quality of life for people with different types of
disabilities.
Question 2. How will manufacturers and developers work to perfect
this technology so that it can truly be a reliable tool for these
individuals?
Answer. These technologies will need to go through extensive
testing and user studies/clinical trials to ensure the safety of the
developments before they are sold to the public or used by the public.
Edge cases need to be tested for events that may not occur frequently
but have the possibility of happening. Once these types of technologies
are developed and tested, then standards need to be established to
ensure ongoing quality of the products for safe use as would be the
case of any product used in medical applications or for consumer use.
Question 3. What more can Congress to do assist with these efforts?
Answer. While research and development is occurring it is important
to not establish highly restrictive legislative policies or it will
stifle the creativity and development by researchers. Once something is
established and has been tested, then it may be necessary to legislate
standards of practice for the protection and safety of the public using
these items. This would be later in the process. Providing funding to
support and an environment supportive of development of these items
would allow the U.S. to stay on the top of research developments that
use artificial intelligence and machine learning. Legislation should be
limited to restrictions and standards for consumer and user safety.
Question 4. As we see machine learning and AI increasingly embedded
in products and services that we rely on, there are numerous cases of
these algorithms falling short of consumer expectations. For example,
Google and Facebook both promoted fraudulent news stories in the
immediate wake of the Las Vegas Shooting because of their
algorithms.\1\
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\1\ NYT: After Las Vegas Shooting, Fake News Regains Its Megaphone,
Kevin Rose, 10/02/2017 https://www.nytimes.com/2017/10/02/business/las-
vegas-shooting-fake-news.html
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YouTube Kids is a service designed for children, and marketed as
containing videos that are suitable for very young children. In
November, YouTube Kids promoted inappropriate content due to
algorithms.\2\ While the use of machine learning and AI holds limitless
positive potential, at the current point, it faces challenges where we
should not risk getting it wrong.
---------------------------------------------------------------------------
\2\ NYT: On YouTube Kids, Startling Videos Slip Past Filters, Sapna
Maheshwari, 11/04/2017 https://www.nytimes.com/2017/11/04/business/
media/youtube-kids-paw-patrol.html
---------------------------------------------------------------------------
Should there be any formal or informal guidelines in place for what
tasks are suitable to be done by algorithms, and which are still too
important or sensitive to turn over; and what more can be done to
ensure better and more accurate algorithms are used as you work to
better develop this technology?
Answer. In cases, where the algorithms are related to safety or
life critical decisions then it may be necessary to have a human in the
loop for sanity checks to ensure the best possible decision is made.
When it comes to children, this would be case when the system needs to
be thoroughly tested with a human involved to ensure the system is
working well and there needs to be testing and validation that occurs
to ensure those ``edge'' cases or rarer situations are also tested for
no matter how unlikely it is for it to occur. Validation and testing
should be performed extensively with adults prior to using the system
with children and then tested with children with adult supervision.
Mistakes can happen, but everything possible needs to be done to
prevent issues that could potentially cause harm. In the case of
decisions to weaponize autonomous systems there should be a human in
the loop when it comes to decisions that impact human lives. There does
need to be established standards and benchmarks to assist developers in
testing to ensure the safety of a product before it is put in the hands
of the public.
Question 5. Machine learning and AI hold great promise for
assisting us in preventing cybersecurity attacks. According to an IBM
survey of Federal IT managers, 90 percent believe that artificial
intelligence could help the Federal Government defend against real-
world cyber-attacks. 87 percent think AI will improve the efficiency of
their cybersecurity workforce.\3\
---------------------------------------------------------------------------
\3\ INFORMATION MANAGEMENT: AI seen as key tool in government's
cybersecurity defense, Bob Violino, 11/30/2017 https://www.information-
management.com/news/artificial-intelligence-seen-as-key-tool-in-
governments-cybersecurity-defense
---------------------------------------------------------------------------
While this is promising, the Federal Government currently faces a
shortage of qualified cybersecurity employees, and to make matters
worse, the pipeline of students studying these topics is not sufficient
to meet our needs. A recent GAO report found that Federal agencies have
trouble identifying skills gaps, recruiting and retaining qualified
staff, and lose out on candidate due to Federal hiring processes.
The George Washington University Center for Cyber & Homeland
Security recently released a report titled ``Trends in Technology and
Digital Security'' which stated:
``Traditional security operations centers are mostly staffed
with tier one analysts staring at screens, looking for unusual
events or detections of malicious activity. This activity is
similar to physical security personnel monitoring video cameras
for intruders. It is tedious for humans, but it is a problem
really well-suited to machine learning.'' \4\
---------------------------------------------------------------------------
\4\ https://cchs.gwu.edu/sites/cchs.gwu.edu/files/downloads/
Fall%202017%20DT%20symposi
um20compendium.pdf
What effect will machine learning and AI will have on
cybersecurity; and how do you think the Federal Government can best
leverage the benefits offered by machine learning and AI to address our
cybersecurity workforce shortage?
Answer. The use of artificial intelligence and machine learning can
definitely help with the tedious task of identifying potential threats
to security. The initial evaluation could be performed using computer
systems and they are adept at detecting anomalies and maybe even ones
that a human may not readily detect. In cases where it is a threat to
life or safety of humans involved, then it may need to verified by a
cybersecurity trained specialist. Overall, the use of good algorithms
and machine learning systems could help fill the gap that has occurred
with the lack of trained cybersecurity personnel. If the initial
detection work can be performed by the computer systems, then it would
require less personnel to verify those findings. Overall, the use of
well-developed AI and machine learning systems could be leveraged to
address some of the workforce shortage issues associated with
cybersecurity professionals. It is also important to better recruit for
these programs and to show the benefits of being involved in this type
of career. There are limitations to the government hiring practices and
payscales, but these can be overcome. It may require changes though to
these practices to entice students entering and choosing their career
fields to consider careers in these areas.
______
Response to Written Question Submitted by Hon. Tom Udall to
Daniel Castro
Question. In your testimony, you discussed how regulators will not
need to intervene because the private sector will address artificial
intelligence's problems--such as bias and discrimination. However,
there have been studies that show implicit bias even when artificial
intelligence is deployed. For example, a study \1\ about using AI to
evaluate resumes found that candidates with names associated with being
European American were 50 percent more likely to be offered an
interview than candidates with names associated with being African-
American. What role should the Federal Government play where there is
implicit bias and discrimination--particularly when companies are
required to be ``Equal Opportunity Employers''?
---------------------------------------------------------------------------
\1\ http://www.sciencemag.org/news/2017/04/even-artificial-
intelligence-can-acquire-biases-against-race-and-gender
---------------------------------------------------------------------------
Answer. This is an important question. To clarify, regulators will
need to continue to intervene to address specific policy goals, such as
ensuring non-discrimination in hiring practices. However, policymakers
do not necessarily need to create new laws and regulations only for AI
to achieve those goals. Existing laws that make these practices illegal
still apply, regardless of whether or not a company uses AI to
discriminate against a protected class. For example, a company cannot
circumvent its obligations in Title VII of the Civil Rights Act to
discriminate against a particular race in its hiring practices simply
by using an algorithm to review job applicants.
There are additional steps policymakers can take to reduce bias.
One way to assess bias, whether it be in analog processes or digital
algorithms, is to have businesses conduct disparate impact analyses.
For example, if a company is using an AI system to screen job
applicants, and it has concerns about potential racial bias, it should
test this system to assess its accuracy. If government agencies are
early adopters of such AI-driven services, they can help identify
potential areas of concerns. However, disparate impact analysis is only
possible if organizations have data available to them. Moreover,
regulators can also identify practices that are known to have a
disparate impact, such as using certain criteria for making a credit or
housing decision, and discouraging businesses from using those
methods.\2\
---------------------------------------------------------------------------
\2\ Travis Korte and Daniel Castro, ``Disparate Impact Analysis is
Key to Ensuring Fairness in the Age of the Algorithm,'' Center for Data
Innovation (2015), http://datainnovation.org/2015/01/disparate-impact-
analysis-is-key-to-ensuring-fairness-in-the-age-of-the-algorithm/.
---------------------------------------------------------------------------
In addition, there are likely areas where additional policy is
needed to protect workers. For example, Congress should consider
passing laws such as the Employment Non-Discrimination Act (ENDA) to
ensure that data about sexual orientation and gender identity cannot be
used to unfairly harm workers.\3\ These types of laws address specific
concerns of vulnerable populations, but they do not apply only to AI.
---------------------------------------------------------------------------
\3\ For more on this topic as it relates to data, see: Joshua New
and Daniel Castro, ``Accelerating Data Innovation: A Legislative Agenda
for Congress,'' Center for Data Innovation (2015), http://
datainnovation.org/2015/05/accelerating-data-innovation-a-legislative-
agenda-for-congress/.
---------------------------------------------------------------------------
Finally, policymakers should recognize that using AI can often
reduce discrimination by limiting the potential for both implicit and
explicit human bias. For example, a company that uses AI to screen
applicants has the potential to reduce implicit bias of human managers
in hiring practices. And while AI systems may not be perfect at the
outset, as people identify problems, they will be able to more quickly
resolve these issues. The same is not true for strictly human
processes, where eliminating bias, is much more difficult.
______
Response to Written Question Submitted by Hon. Gary Peters to
Daniel Castro
Question. Many have predicted that AI will have a profound effect
on the labor market. Most predict that low-wage, routine-based jobs
will be under the most pressure for replacement by AI. Meanwhile,
recent advancements in technology has led to job creation that will
mostly require highly-skilled, highly-educated workers. What evidence
have you seen regarding businesses incorporating this labor shift into
their business plans?
Answer. Some of these predictions are not based on sound analysis.
Bureau of Labor Statistics (BLS) projections show that the fastest
growing jobs are not in high-skilled occupations. For example, the
industry that BLS projects will have the most job growth between 2016-
2026 is the ``food services and drinking places'' industry.\4\ These
are not high-wage jobs. Increased use of AI can yield higher rates of
automation and hopefully fewer of these low-wages jobs. The way to
achieve higher-wage jobs is by increasing productivity. In particular,
increasing productivity in low-skill jobs will grow wages in these
occupations.
---------------------------------------------------------------------------
\4\ See ``Projections of industry employment, 2016-2026,'' Bureau
of Labor Statistics, https://www.bls.gov/careeroutlook/2017/article/
projections-industry.htm.
---------------------------------------------------------------------------
Some companies have taken steps to address disruption in the
workforce. For example, Google and Facebook have made substantial
commitments to funding for job retraining programs.\5\ However,
overall, U.S. companies are investing less in training now than they
were 15 years ago. A knowledge tax credit, where corporates receive a
credit for qualified expenditures on worker training, would help
address this problem.\6\
---------------------------------------------------------------------------
\5\ ``Google pledges $1 billion to prepare workers for
automation,'' Engadget, October 13, 2017, https://www.engadget.com/
2017/10/13/grow-with-google/.
\6\ See Rob Atkinson, ``How a knowledge tax credit could stop
decline in corporate training,'' The Hill, http://thehill.com/blogs/
pundits-blog/finance/235018-how-a-knowledge-tax-credit-could-stop-
decline-in-corporate.
---------------------------------------------------------------------------
______
Response to Written Questions Submitted by Hon. Maggie Hassan to
Daniel Castro
Question 1. Artificial intelligence, or AI, holds tremendous
promise for individuals who experience disabilities. For example,
Google and Microsoft have technologies to process language and speech
and translate it into a text format to assist individuals who are deaf
and hard of hearing.
Other technologies will go even further to improve the lives of
people with disabilities and I would like to learn more from the panel
about what we can expect.
What other specific technologies are you aware of in the AI space
that will help people who experience disabilities?
Answer. AI will have widespread benefits for people with
disabilities. As noted in the question, one of the major areas of
impact AI will have is by allowing more people to interface with
computer systems using their voice, instead of a keyboard. In
particular, the combination of AI with the Internet of Things, will
give people with many types of disabilities a better quality of life as
they will now be able to control more of the world around them. These
functions will allow more people with disabilities to participate in
the workforce, go to school, and be more active in their communities.
In addition, AI can be used to create smart agents that automate
specific tasks, such as scheduling meetings, setting a thermostat, or
re-ordering groceries. While these types of actions are conveniences
for some people, for people with significant disabilities, they can be
empowering and allow individuals significantly more autonomy and
independence.
Question 2. How will manufacturers and developers work to perfect
this technology so that it can truly be a reliable tool for these
individuals?
Answer. One way to improve is by having industry work more closely
with different populations of people with disabilities throughout the
design and testing of new products. Working closely with different
groups helps developers better anticipate user needs and pursue
universal design.
Question 3. What more can Congress to do assist with these efforts?
Answer. One significant challenge is that the need to design for
accessibility for people with disabilities is still underappreciated
among technologists. One way to change this is to address this problem
at the colleges and universities training the next generation of
computer scientists and engineers. For example, Congress could
establish NSF-funded Centers of Excellence for Accessible Design to
prioritize this skillset and develop more curriculum. In addition,
Congress should explore ways to encourage and support more people with
disabilities to pursue careers in technology-related fields so they can
be involved from the outset in the design and creation of more
technologies. Finally, Congress should work to increase access to
technology for people with disabilities, including by ensuring that
programs designed to close the digital divide, such as PC or Internet
access, are updated for newer technologies.
Question 4. As we see machine learning and AI increasingly embedded
in products and services that we rely on, there are numerous cases of
these algorithms falling short of consumer expectations. For example,
Google and Facebook both promoted fraudulent news stories in the
immediate wake of the Las Vegas Shooting because of their
algorithms.\7\ YouTube Kids is a service designed for children, and
marketed as containing videos that are suitable for very young
children. In November, YouTube Kids promoted inappropriate content due
to algorithms.\8\ While the use of machine learning and AI holds
limitless positive potential, at the current point, it faces challenges
where we should not risk getting it wrong. Should there be any formal
or informal guidelines in place for what tasks are suitable to be done
by algorithms, and which are still too important or sensitive to turn
over; and what more can be done to ensure better and more accurate
algorithms are used as you work to better develop this technology?
---------------------------------------------------------------------------
\7\ NYT: After Las Vegas Shooting, Fake News Regains Its Megaphone,
Kevin Rose, 10/02/2017 https://www.nytimes.com/2017/10/02/business/las-
vegas-shooting-fake-news.html
\8\ NYT: On YouTube Kids, Startling Videos Slip Past Filters, Sapna
Maheshwari, 11/04/2017 https://www.nytimes.com/2017/11/04/business/
media/youtube-kids-paw-patrol.html
---------------------------------------------------------------------------
Answer. AI, much like humans, is fallible. There should always be
some oversight of AI, just as there should always be some oversight of
humans. It is not a problem if AI systems make mistakes, unless these
mistakes go undetected. So the key objective, whether a decision is
being made by a computer of a human, is whether there is sufficient
oversight appropriate to the level of risk for the individuals
involved. This will likely be context dependent. This is one reason why
it is inappropriate to talk about industry-wide regulation of AI, and
much more appropriate to talk about industry-specific regulations of
AI. For example, the Department of Transportation may have specific
requirements for the types of oversight it wants for autonomous
vehicles that looks very different than the type of oversight the
Securities and Exchange Commission needs for AI-driven stock trading.
Question 5. Machine learning and AI hold great promise for
assisting us in preventing cybersecurity attacks. According to an IBM
survey of Federal IT managers, 90 percent believe that artificial
intelligence could help the Federal Government defend against real-
world cyber-attacks. 87 percent think AI will improve the efficiency of
their cybersecurity workforce.\9\ While this is promising, the Federal
Government currently faces a shortage of qualified cybersecurity
employees, and to make matters worse, the pipeline of students studying
these topics is not sufficient to meet our needs. A recent GAO report
found that Federal agencies have trouble identifying skills gaps,
recruiting and retaining qualified staff, and lose out on candidate due
to Federal hiring processes. The George Washington University Center
for Cyber & Homeland Security recently released a report titled
``Trends in Technology and Digital Security'' which stated:
---------------------------------------------------------------------------
\9\ INFORMATION MANAGEMENT: AI seen as key tool in government's
cybersecurity defense, Bob Violino, 11/30/2017 https://www.information-
management.com/news/artificial-intelligence-seen-as-key-tool-in-
governments-cybersecurity-defense
``Traditional security operations centers are mostly staffed
with tier one analysts staring at screens, looking for unusual
events or detections of malicious activity. This activity is
similar to physical security personnel monitoring video cameras
for intruders. It is tedious for humans, but it is a problem
really well-suited to machine learning.'' \10\
---------------------------------------------------------------------------
\10\ https://cchs.gwu.edu/sites/cchs.gwu.edu/files/downloads/
Fall%202017%20DT%20symposi
um%20compendium.pdf
What effect will machine learning and AI will have on
cybersecurity; and how do you think the Federal Government can best
leverage the benefits offered by machine learning and AI to address our
cybersecurity workforce shortage?
Answer. AI is very good at specific tasks, such as pattern
recognition and anomaly detection. This means that it will be useful
for identifying attacks in real time, and it will be an especially
important line of defense against zero-day attacks (i.e., attacks that
use a previously undisclosed vulnerability). AI might also help
developers eliminate certain types of vulnerabilities which may be
identifiable at the outset, much like a spell-checker or grammar-
checker can review documents. However, AI will not be a panacea, as
many cybersecurity risks are the result of poor implementation and a
lack of adherence to best practices.
______
Response to Written Questions Submitted by Hon. Gary Peters to
Victoria Espinel
Question 1. I am concerned by recent reports in Nature, The
Economist, and Wall Street Journal about large tech firms monopolizing
the talent in AI and machine learning. This concentration of talent can
lead to several negative outcomes including long-term wage stagnation
and income inequality.
In your opinion, what steps or incentives might mitigate this
concentration, encourage AI-experts to work at small and medium
enterprises, or launch their own start-up with the goal of growing a
business (rather than having a goal of being bought out by one of the
tech giants)? Similarly, what incentives might encourage AI experts to
become educators and trainers to help develop the next generation of AI
experts?
How can the Federal Government compete with the tech giants to
attract experts needed to develop and implement AI systems for defense
and civil applications?
Answer. Artificial intelligence (AI) is a burgeoning field, with
market dynamics that are quickly evolving. While the competition for AI
expertise is certainly fierce, it is important to remember that the
economic benefits of AI will be spread throughout the economy. By
helping people make better data-driven decisions, AI is stimulating
growth in every industry sector. It is helping to optimize
manufacturing, improve supply chains, secure networks, and enhance
products and services.
The history of the technology industry suggests that innovation
will continue to emerge from enterprises of all sizes. Indeed, BSA's
membership is a testament to just how fiercely competitive the
enterprise technology landscape is. Datastax, DocuSign, Salesforce,
Splunk and Workday are just a few of the young companies that have
disrupted the industry over the past 10 years and contributed to a wave
of innovation that has made the U.S. software industry the envy of the
world. Moreover, despite intense competition for AI expertise, small
and medium-sized firms continue to play an incredibly important role in
driving AI innovation. In fact, a recent study found that there are
currently more than 2,000 AI startups that have raised almost $30
billion in funding.\1\
---------------------------------------------------------------------------
\1\ See Vala Afshar, AI Is Tranformational Technology and Major
Sector Disruptor, Huffington Post (Dec. 5, 2017), https://
www.huffingtonpost.com/entry/ai-is-transformational-technology-and-
major-sector_us_5a259dbfe4b05072e8b56b6e.
---------------------------------------------------------------------------
Because AI will be a huge driver of the global economy in the years
ahead, it is vital that we examine the important issues that you have
raised to ensure that the United States remains the global hub for AI
innovation. There are three specific ways in which the government can
increase the talent pool and attract that talent to the government for
defense and civil applications. First, the government should increase
its commitment to STEM education so that the United States prepares
more men and women for AI-related careers. As part of this effort,
government agencies can explore partnerships with academic institutions
to provide internships to students studying in this field, which could
pique their interests in pursuing careers in public service, and
develop opportunities for academic researchers to share their technical
expertise with government agencies. Second, the government should
increase its funding for AI research, providing targeted investments
into the ``high risk, high reward'' areas of basic research that are
typically underfunded by the private sector.\2\ Third, the government
should be ambitious in its goals. The greater the vision for how AI
will improve government services and capabilities, the better it will
do in attracting talent.
---------------------------------------------------------------------------
\2\ See Jason Furman, Is this Time Different? The Opportunities and
Challenges of Artificial Intelligence, AI Now: The Social and Economic
Implications of Artificial Intelligence in the Near Term (July 7,
2016), available at https://goo.gl/pzFDYw (``In 2015, American
businesses devoted almost 1.8 percent of GDP to research and
development, the highest share on record. But government investments in
R&D have fallen steadily as a share of the economy since the 1960s.
While business investment is critical, it is not sufficient. Basic
research discoveries often have great social value because of their
broad applicability, but there tends to be underinvestment in basic
research by private firms because it is difficult for a private firm to
appropriate the gains from such research. In fact, while the private
sector accounts for roughly two-thirds of all spending on R&D, it is
important to keep in mind that it largely invests in applied research
while the Federal Government provides 60 percent of the funding for
basic research.'').
Question 2. Many have predicted that AI will have a profound effect
on the labor market. Most predict that low-wage, routine-based jobs
will be under the most pressure for replacement by AI. Meanwhile,
recent advancements in technology has led to job creation that will
mostly require highly-skilled, highly-educated workers. What evidence
have you seen regarding businesses incorporating this labor shift into
their business plans?
Answer. The benefits of AI will be widespread, likely enhancing
operations in every industry. As a result, AI also will likely create
shifts in the labor market across the economy. The precise impact of AI
on employment is uncertain. However, it is clear that AI will create
new opportunities within existing jobs and new roles that require
skills that the current workforce does not yet have. As a result, many
BSA companies have launched initiatives to train employees, youth, and
military veterans to help meet the demands of the future labor market.
BSA would like to work with Congress to ensure we have the right
programs and resources in place for the jobs of the future. We would be
happy to come in and discuss with you the initiatives of the software
industry that address this important issue.
______
Response to Written Questions Submitted by Hon. Maggie Hassan to
Victoria Espinel
Question 1. Artificial intelligence, or AI, holds tremendous
promise for individuals who experience disabilities. For example,
Google and Microsoft have technologies to process language and speech
and translate it into a text format to assist individuals who are deaf
and hard of hearing. Other technologies will go even further to improve
the lives of people with disabilities and I would like to learn more
from the panel about what we can expect. What other specific
technologies are you aware of in the AI space that will help people who
experience disabilities?
Answer. There are numerous ways in which AI is being used to
improve the lives of people who experience disabilities. Below, I
highlight a few examples.
Visual impairment--Microsoft recently released an
intelligent camera app that uses a smartphone's built-in camera
functionality to describe to low-vision individuals the objects
that are round them. See Microsoft, Seeing AI, https://
www.microsoft.com/en-us/seeing-ai/. The app opens up new
possibilities for the visually impaired to navigate the world
with more independence.
Autism--IBM researchers are using AI to develop tools that
will help people with cognitive and intellectual disabilities,
such as autism, by breaking down complex sentences and phrases
to help them better understand normal speech and communicate
more effectively. See https://www-03.ibm.com/able/content-
clarifier.html.
Accessible public transportation--As part of a public-
private partnership, an innovative project is underway that
aims to help disabled people and those with special needs
access public transportation by providing real-time information
through an Internet of Things system that helps them find the
right track, platform, train, and place to board, and alerts
them when to disembark. See David Louie, Artificial
Intelligence Research Is Helping the Disabled Use Public
Transportation, (July 12, 2017), http://abc7news.com/
technology/ai-being-used-to-help-disabled-using-public-
transportation/2210112/.
Mobility Impairments--Microsoft's Windows 10 operating
system introduced Eye Control, a built-in eye tracking feature
that enables people with motor neurone disease and other
mobility impairments to navigate their computers. See Tas
Bindi, Microsoft Using AI to Empower Living With Disabilities,
Zdnet (Nov. 15, 2017), http://www.zdnet.com/article/microsoft-
using-ai-to-empower-people-living-with-disabilities/.
Alzheimer's--Researchers in Italy and Canada have developed
machine-learning algorithms to help identify patients that are
at risk of developing Alzheimer's. In early tests, the
technology has identified changes in the brain that lead to
Alzheimer's almost a decade before clinical symptoms would
appear. See Daisy Yuhas, Doctors Have Trouble Diagnosing
Alzheimer's. AI Doesn't, NBC News (Oct. 30, 2017), https://
www.nbcnews.com/mach/science/doctors-have-trouble-diagnosing-
alzheimer-s-ai-doesn-t-ncna815561.
As researchers continue to apply AI to new settings, the myriad
ways in which AI is used to enhance the lives of people with
disabilities will only increase.
Question 2. How will manufacturers and developers work to perfect
this technology so that it can truly be a reliable tool for these
individuals?
Answer. BSA members that design and offer AI products and services
have strong incentives to ensure that the technology is reliable, as
they understand that building trust and confidence in AI systems is
integral to successful and widespread deployment of AI services.
There are a number of strategies that companies already employ to
accomplish this objective. For example, one key priority is ensuring
access to vast, robust, and representative data sets. Because AI
technologies process and learn from data inputs, ensuring sufficient
quantity and quality of data used to train AI systems is very important
to enhancing the reliability of these services.
In addition, another key step companies take to enhance reliability
is testing their AI systems to ensure that they operate as intended,
and making appropriate adjustments where they identify errors.
Companies also recognize the need to protect AI systems from
cyberattacks and are investing heavily in the development of advanced
security tools.
As companies continue to seek to expand the capabilities of AI
technologies, investment in research and development will continue to
be important to unleash the full potential of innovation, strengthen
cybersecurity, and enhance the overall reliability of AI systems.
Question 3. What more can Congress to do assist with these efforts?
Answer. Congress can play a very important role in facilitating the
deployment of AI services that help people with disabilities by
ensuring, more broadly, that the U.S. maintains a flexible policy
framework that spurs innovation in AI.
Specifically, as I highlighted in my testimony, I think Congress
can assist with these efforts in three key ways. First, Congress should
pass the OPEN Government Data Act, which recognizes that government-
generated data is a national resource that can serve as a powerful
engine for creating new jobs and a catalyst for economic growth, and
that it is incredibly valuable in fostering innovation in AI and other
data-driven services.
Second, Congress should support efforts to promote digital trade
and facilitate data flows. In a global economy, real-time access to
data around the world has become increasingly critical for AI and other
digital services to function. As a result, Congress should support
modernizing trade initiatives, such as NAFTA, that seek to facilitate
digital trade and limit inappropriate restrictions on cross-border data
transfers.
Third, Congress should promote U.S. investment in AI research,
education, and workforce development to ensure that the U.S. remains
globally competitive. Strategic investment in education and workforce
development can help ensure that the next generation and our current
workforce are prepared for the jobs of the future. In addition,
promoting public sector and incentivizing private sector research will
be essential to unlocking additional capabilities that AI can provide.
Question 4. As we see machine learning and AI increasingly embedded
in products and services that we rely on, there are numerous cases of
these algorithms falling short of consumer expectations. For example,
Google and Facebook both promoted fraudulent news stories in the
immediate wake of the Las Vegas Shooting because of their
algorithms.\3\ YouTube Kids is a service designed for children, and
marketed as containing videos that are suitable for very young
children. In November, YouTube Kids promoted inappropriate content due
to algorithms.\4\ While the use of machine learning and AI holds
limitless positive potential, at the current point, it faces challenges
where we should not risk getting it wrong. Should there be any formal
or informal guidelines in place for what tasks are suitable to be done
by algorithms, and which are still too important or sensitive to turn
over; and what more can be done to ensure better and more accurate
algorithms are used as you work to better develop this technology?
---------------------------------------------------------------------------
\3\ NYT: After Las Vegas Shooting, Fake News Regains Its Megaphone,
Kevin Rose, 10/02/2017 https://www.nytimes.com/2017/10/02/business/las-
vegas-shooting-fake-news.html
\4\ NYT: On YouTube Kids, Startling Videos Slip Past Filters, Sapna
Maheshwari, 11/04/2017 https://www.nytimes.com/2017/11/04/business/
media/youtube-kids-paw-patrol.html
---------------------------------------------------------------------------
Answer. Because AI is ultimately a technology that is intended to
help people and organizations make better uses of data, I would be
reluctant to prescribe any bright line rules about when its use may or
may not be appropriate. However, it is important for companies that
develop AI systems, and their customers, to consider the unique risks
and potential unintended consequences that can arise when AI is
deployed in particular settings. While AI is an invaluable tool for
making sense of large quantities of data, there are settings where the
intuition of subject matter experts will remain important. For
instance, while AI systems certainly have an important role to play in
helping to diagnose patients, they are a resource for a medical
professional to consider in making a diagnosis or prescribing
treatment, they should not be replacing a doctor's judgment.
Question 5. Machine learning and AI hold great promise for
assisting us in preventing cybersecurity attacks. According to an IBM
survey of Federal IT managers, 90 percent believe that artificial
intelligence could help the Federal Government defend against real-
world cyber-attacks. 87 percent think AI will improve the efficiency of
their cybersecurity workforce.\5\
---------------------------------------------------------------------------
\5\ INFORMATION MANAGEMENT: AI seen as key tool in government's
cybersecurity defense, Bob Violino, 11/30/2017 https://www.information-
management.com/news/artificial-intelligence-seen-as-key-tool-in-
governments-cybersecurity-defense
---------------------------------------------------------------------------
While this is promising, the Federal Government currently faces a
shortage of qualified cybersecurity employees, and to make matters
worse, the pipeline of students studying these topics is not sufficient
to meet our needs. A recent GAO report found that Federal agencies have
trouble identifying skills gaps, recruiting and retaining qualified
staff, and lose out on candidate due to Federal hiring processes.
The George Washington University Center for Cyber & Homeland
Security recently released a report titled ``Trends in Technology and
Digital Security'' which stated:
``Traditional security operations centers are mostly staffed
with tier one analysts staring at screens, looking for unusual
events or detections of malicious activity. This activity is
similar to physical security personnel monitoring video cameras
for intruders. It is tedious for humans, but it is a problem
really well-suited to machine learning.'' \6\
---------------------------------------------------------------------------
\6\ https://cchs.gwu.edu/sites/cchs.gwu.edu/files/downloads/
Fall%202017%20DT%20symposi
um%20compendium.pdf
What effect will machine learning and AI will have on
cybersecurity; and how do you think the Federal Government can best
leverage the benefits offered by machine learning and AI to address our
cybersecurity workforce shortage?
Answer. AI tools are revolutionizing network security, helping
analysts parse through hundreds of thousands of security incidents per
day to weed out false positives and identify threats that warrant
further attention by network administrators. By automating responses to
routine incidents and enabling security professionals to focus on truly
significant threats, AI-enabled cyber tools are helping enterprises
stay ahead of their malicious adversaries. For instance, AI has helped
an enterprise security operations center ``reduce the time to remediate
spearphishing attacks from there hours per incident to less than two
minutes per incident.'' \7\ Importantly, AI is also helping to train
the next generation of security analysts, teaching them to more quickly
identify threats that need to be escalated through the chain of
command.\8\ Greater deployment of AI is therefore a critical factor for
addressing the cyber workforce shortage, which experts now estimate
will climb 1.8 million positions by 2022.
---------------------------------------------------------------------------
\7\ See Robert Lemos, AI Is Changing SecOps: What Security Analysts
Need to Know, TechBeacon (Dec. 19, 2017), https://techbeacon.com/ai-
changing-secops-what-security-analysts-need-know.
\8\ Id.
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However, AI alone will not solve the cyber workforce shortage. It
is therefore incumbent on governments and industry to work
collaboratively to grow the pipeline of cyber talent. To that end, BSA
recently launched a new cybersecurity agenda \9\ that highlights four
pathways for developing a 21st century cybersecurity workforce:
---------------------------------------------------------------------------
\9\ BSA/The Software Alliance, A Cybersecurity Agenda for the
Connected Age, available at www.bsa.org/ /media/Files/Policy/
BSA_2017CybersecurityAgenda.pdf.
Increase access to computer science education: Expand
cybersecurity education for K-12 as well as in undergraduate
computer science programs, increase scholarships, and
---------------------------------------------------------------------------
incentivize minority students.
Promote alternative paths to cybersecurity careers: Launch
careers through apprenticeship programs, community colleges,
cybersecurity ``boot camps,'' and government or military
service.
Modernize training for mid-career professionals: Reform
Trade Adjustment Assistance, and update other mid-career re-
training programs, to provide American workers with high-demand
cybersecurity and IT skills as digitalization transforms the
global economy.
Improve the exchange of cybersecurity professionals between
the government and private sector: Enable private sector
experts to join the government for periodic or short-term
assignments.
______
Response to Written Question Submitted by Hon. Amy Klobuchar to
Dr. Dario Gil, Ph.D.
Question. While I was at the hearing there was significant
discussion about the future security applications for machine learning
and artificial intelligence. As the Ranking Member on the Rules
Committee, I am working with Senators Lankford, Harris and Graham on a
bill to upgrade our election equipment to protect against cyber-
attacks. The Department of Homeland Security recently confirmed that
hackers targeted 21 states' election systems in the run-up to the 2016
election. As we prepare for 2018 and beyond, we must ensure that our
election systems are secure, both from a hardware and a software
perspective because election security is national security. Dr. Gil,
can artificial intelligence and machine learning be used to identify
and prevent cyber-attacks?
Answer. The power of AI, like most machine learning techniques,
lies in identifying broader trends, building models of what normal and
expected behavior is and flagging anomalies. There has been tremendous
value shown by deploying AI in the field of security, and we use it in
a plethora of use cases: in, applications to flag systems and networks,
to monitor for anomalies and raise alerts when these behaviors change.
Such anomalous behavior may indicate an attack (or a benign error). AI
has also been leveraged for generalizations to make it easier to
identify new instances of known attacks. It can be used to learn about
malware and exploits of vulnerabilities and can use that to detect new
infections and intrusions better than rule-based systems. AI and AI-
based techniques can also help in hardening security protections to
make it more difficult for attackers to successfully exploit a system.
For example, automating tests (fuzzing) to probe for vulnerabilities
using reinforcement learning can be more efficient than an exhaustive
scan. It should be borne in mind that while there is no panacea for
security, AI is a very powerful tool that can be employed to increase
security when combined with a standard suite of best practices.
______
Response to Written Questions Submitted by Hon. Tom Udall to
Dr. Dario Gil, Ph.D.
Question 1. As you are aware, New Mexico is home to two national
laboratories--Sandia and Los Alamos. Can you speak of any partnership
you have with the national laboratories?
Answer. We have had, and continue to have, a number of partnerships
with Los Alamos and Sandia National Labs. At this time, we cannot
comment on individual projects, but we value the joint work we have
with the labs and all of our external partners.
Question 2. Can you speak of some of the ways that government
funded artificial intelligence development is now being used in the
private sector?
Answer. Government-funded AI development is currently being used by
IBM in the following ways:
U.S. Army Research Labs funded the development of
technologies for rule-based systems and policy management. The
technology developed is being used now by IBM to improve and
support the ability of employees in a company to configure
computer software to efficiently execute high-volume, highly
transactional process functions, boosting capabilities and
saving time and money.
U.S. Army Research Labs funded the development of AI enabled
algorithms for analyzing and understanding properties of large
numbers of moving objects. The technology developed has been
used to create commercial cloud-based services such as The
Weather Company LongShip service to predict the influence of
weather impacts on traffic. It also has been incorporated into
commercial software products such as DB/2.
Looking more broadly, there are a variety of ways in which
government funded research can be used by the private sector. These
include:
Government funded technology has been used to create dual-
purpose technologies--those which serve the needs of the
private sector as well as the needs of the government agency
which sponsored the work. One expression of this is when the
private sector produces a COTS (Custom Off the Shelf) product
which allows the government to meet their specific requirement
at a lower overall cost.
Government funded research (primarily in basic sciences) has
a research horizon which is typically longer than the private
sector. As a result, government funded research has been used
to create technology at the earlier stage. There are many
instances where government support has been used for
technologies at the TRL (Technology Readiness Level) of 1-4,
and the successful ones from this level of exploration have led
to commercialized products at TRL level 5 and above.
Government funded technology has been used to produce open
source software, which private sector companies develop further
and use to create new offerings. In many cases, the open source
software is used by academics and the public at large for
knowledge creation.
Government funded alliances have driven collaboration
between private sector researchers, academics and government
researchers. This cross-fertilization of researchers working
towards a common goal has been beneficial to all parties--
including employment opportunities for students, infusion of
new ideas in industry activities, improvements in government,
and formation of lasting collaborations.
______
Response to Written Question Submitted by Hon. Gary Peters to
Dr. Dario Gil, Ph.D.
Question. A major challenge AI and machine learning developers need
to address is the ability to ensure prolonged safety, security, and
fairness of the systems. This is especially true of systems designed to
work in complex environments that may be difficult to replicate in
training and testing, or systems that are designed for significant
learning after deployment. Dr. Gil, you testified that IBM is looking
to build trust in AI by following a set of principles to guide your
development and use of AI systems. Would you please provide more detail
about how these principles are being implemented? How will these
principles prevent a system designed to learn after deployment from
developing unacceptable behavior over time?
Answer. The currently available AI products, such as factory
robots, personal digital assistants, and healthcare decision support
systems, are designed to perform one narrow task, such as assemble a
product, provide a weather forecast or make a purchase order, or help a
radiologist interpret an X-ray. When these technologies learn after
deployment, they do so in the context of that narrow task, and do not
have the ability to learn other tasks on their own, even similar ones.
The kind of AI systems that can acquire new skills and perform new
cognitive and reasoning tasks autonomously are actively being
researched. This effort does not include only the development of
underlying learning and reasoning capabilities; the AI research
community is actively pursuing the capabilities that would ensure the
safety, security and fairness of these systems.
To start with, the principles of safe design are applied to a wide
variety of engineered systems, such as trains, safety breaks,
industrial plants, flight autopilot systems, and robotic laser surgery.
Some of these principles apply directly to the design of AI systems,
some will be adapted, and new ones will have to be defined. For
example, it is possible to constrain the space of outcomes or actions a
robot can perform, to ensure that it does not accidentally come into
contact with human workers and cause injury. Similarly, robots in
complex environments that encounter completely new situations could be
designed to require human intervention. Another direction is to embed
principles of safe and ethical behavior in the AI reasoning mechanisms,
so that they can distinguish between right and wrong actions.
With respect to the fairness of the AI systems, we are currently
pursuing a range of efforts aimed at developing and embedding in our
services and offerings techniques for bias detection, certification,
and mitigation. For example, we have developed algorithms that can de-
bias training data so that any AI system that learns from such data
does not discriminate against protected groups (e.g., those defined by
race or gender). We also are working on using blockchain to ensure the
integrity of an AI system by making sure that it is secure, auditable
and used as intended. We also are developing capabilities to enhance
the explainability and interpretability of AI systems, so that
unacceptable behaviors can be easily discovered and removed.
IBM has established the following principles for the artificial
intelligence/cognitive era:
Purpose: The purpose of AI and cognitive systems developed and
applied by the IBM company is to augment human intelligence. Our
technology, products, services and policies will be designed to enhance
and extend human capability, expertise and potential. Our position is
based not only on principle but also on science. Cognitive systems will
not realistically attain consciousness or independent agency. Rather,
they will increasingly be embedded in the processes, systems, products
and services by which business and society function--all of which will
and should remain within human control.
Transparency: For cognitive systems to fulfill their world-changing
potential, it is vital that people have confidence in their
recommendations, judgments and uses. Therefore, the IBM company will
make clear:
When and for what purposes AI is being applied in the
cognitive solutions we develop and deploy.
The expertise that informs the insights of cognitive
solutions, as well as the methods used to train those systems
and solutions.
The principle that clients own their own business models and
intellectual property and that they can use AI and cognitive
systems to enhance the advantages they have built. We will work
with our clients to protect their data and insights, and will
encourage our clients, partners and industry colleagues to
adopt similar practices.
Skills: The economic and societal benefits of this new era will not
be realized if the human side of the equation is not supported. This is
uniquely important with cognitive technology, which augments human
intelligence and expertise and works collaboratively with humans.
Therefore, the IBM company will work to help students, workers and
citizens acquire the skills and knowledge to engage safely, securely
and effectively in a relationship with cognitive systems, and to
perform the new kinds of work and jobs that will emerge in a cognitive
economy.
Data: Since AI is heavily based on data, IBM has developed a
framework of best practices for data stewardship \1\ that ensures great
care and responsibility in data ownership, storage, security, and
privacy. IBM abides by these practices and, as a result, serves as a
data steward providing transparent and secure services. For example, we
write client agreements with full transparency and will not use client
data unless they agree to such use. We will limit that use to the
specific purposes clearly described in the agreement. IBM does not put
`backdoors' in its products for any government agency, nor do we
provide source code or encryption keys to any government agency for the
purpose of accessing client data.
---------------------------------------------------------------------------
\1\ IBM: Data Responsibility@IBM, Khttps://www.ibm.com/blogs/
policy/wp-content/uploads/2017/10/IBM_DataResponsibility-A4_WEB.pdf
---------------------------------------------------------------------------
We are working on a range of efforts aimed at developing and
embedding in our services and offerings techniques for bias detection,
certification, and mitigation. For example, we are working on improving
the accuracy of directly interpretable decision-support algorithms,
such as decision trees and rule sets, as well as enhancing the
interpretability of deep learning neural net models.
Moreover, as we develop innovative AI systems, we are guided by the
principles of safety engineering. Some of these principles could be
directly applied to the design of AI systems, some will be adapted, and
new ones will have to be defined. For example, robots in complex
environments that encounter completely new situations could be designed
to require human intervention. Another direction is to embed principles
of safe and ethical behavior in their reasoning mechanisms, so they can
distinguish between right and wrong actions.
Finally, we are working to develop AI systems that act according to
human values that are relevant for the scenarios and communities in
which such systems will be deployed. This means constraining the
learning, reasoning and optimization machinery inside AI systems to
behavioral constraints. These constraints will ensure that the actions
of the AI system comply with values and guidelines that humans define
as appropriate for the specific use case and application. Such
behavioral constraints should be learned offline (i.e., by training
the system with data or via simulation), modified only by humans, and
given a higher priority compared to online policies (outcomes that an
AI system learns post-deployment, which are based on reinforcement
learning or other machine learning approaches aimed at reward
maximization and optimization).
______
Response to Written Questions Submitted by Hon. Maggie Hassan to
Dr. Dario Gil, Ph.D.
Question 1. Artificial intelligence, or AI, holds tremendous
promise for individuals who experience disabilities. For example,
Google and Microsoft have technologies to process language and speech
and translate it into a text format to assist individuals who are deaf
and hard of hearing. Other technologies will go even further to improve
the lives of people with disabilities and I would like to learn more
from the panel about what we can expect. What other specific
technologies are you aware of in the AI space that will help people who
experience disabilities?
Answer. AI technologies are enabling many exciting new assistive
functions by enhancing machines' ability to see, hear, interpret
complex signals, and operate in the real world through action and
dialog. Essential building blocks for these new capabilities are
machine vision, speech to text, text to speech, natural language
understanding and generation, emotion recognition, and machine learning
to interpret sensor data.
For example, with AI vision, it is now becoming possible to
describe an image, a local environment, or a video to a person with
visual impairment. Further, these technologies will soon support
wearable assistants that can recognize people, objects, landmarks and
obstacles in the environment, and guide a person safely to an
unfamiliar destination.
AI speech to text capabilities, coupled with natural language
understanding, enable a quadriplegic individual to control their
environment through speech commands, providing a new level of autonomy.
Machine learning techniques can translate brain and nerve signals into
commands for prosthetic limbs and convey a sense of touch.
AI natural language understanding and generation enable
communication of knowledge in the form most easily understood by an
individual, whether that means generating a description of a graph for
a blind person, reading text aloud for a person with dyslexia,
simplifying a complex document for a person with an intellectual
disability, or, one day, translating between spoken languages and sign
languages used by people who are deaf and hard of hearing.
AI embedded in autonomous vehicles, intelligent wheelchairs and
interactive assistance robots will provide physical independence and
assistance for many. For example, IBM, the CTA (Consumer Technology
Association) Foundation, and Local Motors are exploring applications of
Watson technologies to developing the world's most accessible self-
driving vehicle, able to adapt its communication and personalize the
overall experience to suit each passenger's unique needs.
Machine learning on sensor data from instrumented environments can
support an older adult in living independently and safely at home by
learning their normal patterns of behavior and providing assistance and
alerts.
Just as importantly, AI technologies will benefit people with
disabilities by analyzing websites and applications, finding and fixing
accessibility problems in a more automated way than was previously
possible.
Question 2. How will manufacturers and developers work to perfect
this technology so that it can truly be a reliable tool for these
individuals?
Answer. Perfecting AI technologies will take significant
experimentation and depends on the availability of data. For these core
technologies to be reliable, applications for people with disabilities
should be explored as early as possible and used to drive requirements.
This includes consulting with, and testing by, people with disabilities
in realistic environments.
At IBM, we are exploring the potential of AI technologies to
support people with disabilities, and older adults through several
initiatives and collaborations. IBM researchers are exploring how
Watson's language-processing software could help people with cognitive
disabilities by simplifying text, how older adults' patterns of
activity can be learned, how a blind person can navigate and find
objects effectively using machine vision, and how AI can enable our
accessibility test tools to move from pointing out problems to actively
suggesting solutions.
Secondly, it is essential that people with disabilities are
represented adequately in training data, to prevent new forms of
discrimination from emerging. For example, an authentication system
based on voice recognition should be able to recognize people with
dysarthric speech. Manufacturers and developers applying AI
technologies should incorporate mechanisms to recognize and gracefully
handle exceptions, falling back on human judgment for cases that are
outside their training.
Question 3. What more can Congress to do assist with these efforts?
Answer. For AI to deliver the promised economic and societal
benefits to a broader range of people, including people with
disabilities, both policy support and public investment from the U.S.
Government are critical.
Given the great diversity of human abilities, it is a challenge for
manufacturers and developers to ensure diversity in training data. For
example, speech recognition training data should ideally include people
who stutter. Government investment in initiatives to make diverse data
broadly available would accelerate our ability to make AI technology
more inclusive, and to apply AI techniques to new accessibility
problems. Government support for controlled studies with people with
disabilities will also accelerate the inclusion of people with
disabilities.
Access to other forms of data is also critical. An indoor-outdoor
navigation system for blind people relies on public outdoor maps, but
indoor maps are privately owned. A centralized mechanism to share such
maps for accessibility purposes would remove a practical barrier to the
widespread use of such systems. AI vision techniques depend on the use
of images or video to describe people and objects to people with visual
impairment. Government leadership is needed to address privacy concerns
of individuals and copyright concerns of organizations over the use of
images of their faces or products for accessibility purposes. There is
a copyright exception for converting books into braille, and a similar
solution could be effective here.
Secondly, policy support can help to counter the danger of new
forms of discrimination. For example, reinstating the Department of
Justice rulemaking on accessibility guidelines for public websites
would emphasize the importance of accessibility, and spur efforts by
industry to include people with disabilities in development.
Most of the examples in question 1 describe ways that AI
technologies can assist people with sensory or physical impairments.
There is a need to foster standards and policies to address the needs
of people with cognitive disabilities, which will encourage application
of AI technologies to these challenges.
Question 4. As we see machine learning and AI increasingly embedded
in products and services that we rely on, there are numerous cases of
these algorithms falling short of consumer expectations. For example,
Google and Facebook both promoted fraudulent news stories in the
immediate wake of the Las Vegas Shooting because of their
algorithms.\2\ YouTube Kids is a service designed for children, and
marketed as containing videos that are suitable for very young
children. In November, YouTube Kids promoted inappropriate content due
to algorithms.\3\ While the use of machine learning and AI holds
limitless positive potential, at the current point, it faces challenges
where we should not risk getting it wrong. Should there be any formal
or informal guidelines in place for what tasks are suitable to be done
by algorithms, and which are still too important or sensitive to turn
over; and what more can be done to ensure better and more accurate
algorithms are used as you work to better develop this technology?
---------------------------------------------------------------------------
\2\ NYT: After Las Vegas Shooting, Fake News Regains Its Megaphone,
Kevin Rose, 10/02/2017 https://www.nytimes.com/2017/10/02/business/las-
vegas-shooting-fake-news.html
\3\ NYT: On YouTube Kids, Startling Videos Slip Past Filters, Sapna
Maheshwari, 11/04/2017 https://www.nytimes.com/2017/11/04/business/
media/youtube-kids-paw-patrol.html
---------------------------------------------------------------------------
Answer. AI is already more capable than humans in narrow domains,
some of which involve delicate decision making. Humanity is not
threatened by them, but many people could be affected by their
decisions. Examples are autonomous online trading agents, media and
news services, and soon autonomous cars. Even though AI algorithms are
usually evaluated based on their accuracy, that is, their ability to
produce correct results, this is only one component of a bigger
picture. We need to be able to assess the impact of their decisions in
the narrow domains where they will function.
To understand the suitability of an AI system with respect to
performing a specific task, one must consider not only their accuracy,
but also the context, the possible errors, and the consequences on the
impacted communities. Furthermore, the assessment of risk should be
carried out with respect to both the risk of ``doing it'' and the risk
of ``not doing it'', as in many fields we already know the consequences
of wrong decisions made by humans. For example, melanoma detection from
skin images is a task that AI algorithms can perform at high levels of
accuracy. Even though there is still a possibility of error, it is
beneficial to deploy such systems in healthcare decision support, in a
way that would augment human decision-making process. On the other
hand, let us consider automated trading systems. A bad decision in
these systems may be (and has been) a financial disaster for many
people. That will also be the case for self-driving cars. Some of their
decisions will be critical and possibly affect lives. Because sectors
like finance and transportation can carry large risks, protections have
always been in place through existing regulations. These existing
protections are properly designed to provide consumer protection even
with the advent of new technologies like AI.
Finally, we believe that in many applications, rather than
considering only fully autonomous AI solutions, the most effective
approach is to build AI systems that support humans and work with them
in performing a task. For example, in a breast cancer detection study,
it has been shown that doctors and AI working together achieve a higher
degree of accuracy than just doctors or AI separately.
Question 5. Machine learning and AI hold great promise for
assisting us in preventing cybersecurity attacks. According to an IBM
survey of Federal IT managers, 90 percent believe that artificial
intelligence could help the Federal Government defend against real-
world cyber-attacks. 87 percent think AI will improve the efficiency of
their cybersecurity workforce.\4\
---------------------------------------------------------------------------
\4\ INFORMATION MANAGEMENT: AI seen as key tool in government's
cybersecurity defense, Bob Violino, 11/30/2017 https://www.information-
management.com/news/artificial-intelligence-seen-as-key-tool-in-
governments-cybersecurity-defense
---------------------------------------------------------------------------
While this is promising, the Federal Government currently faces a
shortage of qualified cybersecurity employees, and to make matters
worse, the pipeline of students studying these topics is not sufficient
to meet our needs. A recent GAO report found that Federal agencies have
trouble identifying skills gaps, recruiting and retaining qualified
staff, and lose out on candidate due to Federal hiring processes.
The George Washington University Center for Cyber & Homeland
Security recently released a report titled ``Trends in Technology and
Digital Security'' which stated:
``Traditional security operations centers are mostly staffed
with tier one analysts staring at screens, looking for unusual
events or detections of malicious activity. This activity is
similar to physical security personnel monitoring video cameras
for intruders. It is tedious for humans, but it is a problem
really well-suited to machine learning.'' \5\
---------------------------------------------------------------------------
\5\ https://cchs.gwu.edu/sites/cchs.gwu.edu/files/downloads/
Fall%202017%20DT%20symposi
um%20compendium.pdf
What effect will machine learning and AI will have on
cybersecurity; and how do you think the Federal Government can best
leverage the benefits offered by machine learning and AI to address our
cybersecurity workforce shortage?
Answer. AI and machine learning will be a disruptive force in the
field of cybersecurity by providing the potential for aiding both in
the defense and protection of critical infrastructure, leveling the
playing field between large nation states and smaller niche players.
From a defensive standpoint, AI has shown promise in automating
defenses, such as probing systems for weaknesses, including software
vulnerabilities and configuration errors. Penetration testing and bug
finding tools have benefited tremendously from AI techniques in
improving their efficiency to more quickly evaluate systems for
weaknesses and increase coverage of the evaluated space. Security
monitoring tools have also benefited greatly from AI and will continue
to do so as AI systems improve. Automation can be leveraged to process
suspicious alerts and events that warrant investigation, performing
many of the rote tasks typically performed by low-level analysts. These
automated tools will provide an analyst a more complete picture of the
events unfolding, highlight meaningful information and context, triage,
and allow the analyst to provide a higher-level response. This can
allow security analysts to investigate far more alerts than are
currently possible, and hopefully make fewer errors in how those alerts
are processed. Security operations can be conducted at machine-scale as
opposed to human-scale.
The Federal Government can learn from the experience in research,
industry and academia in leveraging AI to develop and deploy the next
generation of AI-powered defenses that will be necessary to protect the
Nation's critical infrastructure. This requires significant leadership
and outreach on behalf of the Government to industry and academia on
the following fronts:
Declare AI Leadership in Cyber Security as a national
research and development priority.
Evolve and develop the Nation's cybersecurity strategy to
address the AI-powered threats to critical infrastructure with
AI-powered defenses.
Initiate U.S. Government programs, through various policy
and funding agencies (e.g., OSTP, DARPA, IARPA, NSF, NIST etc.)
to fund and sponsor leading edge research in areas of
intersection between AI and security
Set policies and standards for procurement of next
generation security controls by the U.S. Government.
______
Response to Written Question Submitted by Hon. Amy Klobuchar to
Dr. Edward W. Felten, Ph.D.
Question. Political ads on the Internet are more popular now than
ever. In 2016, more than $1.4 billion was spent on digital
advertisements and experts project that number will continue to
increase. In October, I introduced the Honest Ads Act with Senators
Warner and McCain, to help prevent foreign interference in future
elections and improve the transparency of online political
advertisements. We know that 90 percent of the ads that Russia
purchased were issue ads meant to mislead and divide Americans.
Increasing transparency and accountability online will benefit
consumers and help safeguard future elections. Dr. Felten, can machine
learning be used to help identify issue ads and stop misinformation
from spreading online?
Answer. Yes, machine learning can be useful in several ways.
Machine learning can help to classify the nature or topic of ads, to
distinguish issue ads from others and to characterize the issue being
addressed by an ad. Machine learning can be useful in in determining
the source of an ad, including in identifying when a single source is
trying to disguise itself as a set of separate, independent sources.
More broadly, machine learning can be helpful in identifying
misinformation and disinformation campaigns, and in targeting
countermeasures to maximize the impact on a harmful campaign while
minimizing collateral damage.
Three caveats are in order, however. First, more research will be
necessary to take full advantage of these opportunities. That research
is best done using realistic datasets derived from platforms'
experience with past disinformation campaigns. Second, machine learning
methods will necessarily be less than perfectly accurate. Not only will
they fail to spot some disinformation campaigns, they will also
sometimes misclassify content or a user as malicious when they are in
fact benign. Appropriate use of machine learning in this setting will
require both a careful technical evaluation of the likelihood of
errors, and a policy approach that recognizes the harm that might be
done by errors. Finally, machine learning systems for detecting
anomalies depend on a variety of data sources and signals, and the
success of machine learning depends on the characteristics of those
sources and signals. Real-world data is sometimes erroneous and often
incomplete, in ways that could frustrate the use of machine learning
for this application or render it less accurate. Where data signals
derive from the votes or clicks of users, the resulting system may be
subject to gaming or manipulation, so such signals should be used with
caution, especially in systems that aim to limit disinformation.
______
Response to Written Question Submitted by Hon. Tom Udall to
Dr. Edward W. Felten, Ph.D.
Question. In your testimony, you discussed how adoption of
artificial intelligence can inadvertently lead to biased decisions.
What specific steps should the Federal Government and other users take
to improve the data and ensure that datasets minimize societal bias--
especially with regard to vulnerable populations?
Answer. The results of a machine learning system can only be as
accurate as the dataset on which the system was trained. If a community
is underrepresented in the dataset, relative to its representation in
the population, then that community is likely to be poorly served by
the system, as the system will not put enough weight on the
characteristics of the underrepresented community.
Practitioners should take care to ensure that datasets are
representative of the population, to the extent possible. Where this is
not possible, deficiencies in the dataset should be noted carefully,
and steps should be taken to mitigate the deficiencies. For example, it
is sometimes possible to correct for a group's underrepresentation in a
data analysis or machine learning procedure by putting greater weight
on data points that represent that group. Additional statistical
methods exist that can counteract the effect of non-representative
datasets.
Another common source of error or bias in machine learning occurs
when a system is tasked with learning from examples of past decisions
made by people. If those past decisions were biased, the machine is
likely to learn to replicate that bias. Whenever a system is trained
based on past human decisions, care should be taken to consider the
social and historical context of those past decisions and to look for
indications of bias in the system's output, and anti-bias techniques
should be used in designing or training the system if possible.
In addition to technical measures in the design and use of AI
systems, the possibilities of bias--whether that AI will introduce bias
or that AI will open opportunities to measure and counteract human
bias--should be taken into account in making policy decisions.
Consulting with technical experts, and including technical expertise in
the policymaking conversation, are important steps toward good policy
in this area.
______
Response to Written Question Submitted by Hon. Gary Peters to
Dr. Edward W. Felten, Ph.D.
Question. I am concerned by recent reports in Nature, The
Economist, and Wall Street Journal about large tech firms monopolizing
the talent in AI and machine learning. This concentration of talent can
lead to several negative outcomes including long-term wage stagnation
and income inequality.
In your opinion, what steps or incentives might mitigate this
concentration, encourage AI-experts to work at small and medium
enterprises, or launch their own start-up with the goal of growing a
business (rather than having a goal of being bought out by one of the
tech giants)? Similarly, what incentives might encourage AI experts to
become educators and trainers to help develop the next generation of AI
experts?
How can the Federal Government compete with the tech giants to
attract experts needed to develop and implement AI systems for defense
and civil applications?
Answer. One approach to making smaller companies attractive to top
AI talent is to adopt pro-competition policies generally. Because AI
often relies on large datasets, and those datasets are more likely to
be held by large companies, there may be a natural tendency toward
concentration in AI-focused industry sectors. Public policy can help to
ensure that smaller companies can be viable competitors. The Federal
Government can also provide some large, high quality datasets that may
be useful to individuals and companies of all sizes.
At present, the demand for highly skilled AI experts exceeds the
supply, leading to a scarcity of those experts in all but the best-
funded environments. In the long run, steps to increase the education
and training of AI professionals are the most important means to
strengthen our national talent base and broaden the availability of
expertise.
The talent pipeline can be widened at every stage. At the K-12
level, access to a good computer science course should be available to
every student. A bipartisan coalition of states and nonprofit actors is
working toward this goal. At the university and graduate level, access
to education is limited by the number of trained faculty available to
teach advanced AI and machine learning courses.
It is difficult to underestimate the importance of supporting a
large and robust public research community. This ensures that access to
the latest knowledge and techniques in AI is available to the public
and not limited to a few companies' researchers. It widens the talent
pipeline because AI research funding enables faculty hiring in AI,
which increases the national capacity to train AI leaders. Federally-
funded research projects serve as the main training ground for the next
generation of research leaders.
______
Response to Written Questions Submitted by Hon. Maggie Hassan to
Dr. Edward W. Felten, Ph.D.
Question 1. Artificial intelligence, or AI, holds tremendous
promise for individuals who experience disabilities. For example,
Google and Microsoft have technologies to process language and speech
and translate it into a text format to assist individuals who are deaf
and hard of hearing. Other technologies will go even further to improve
the lives of people with disabilities and I would like to learn more
from the panel about what we can expect. What other specific
technologies are you aware of in the AI space that will help people who
experience disabilities?
Answer. There are many examples, of which I will highlight three
here.
First, self-driving vehicles will improve mobility and lower the
cost of transportation for people who are unable to drive. These
vehicles will have major safety benefits in the long run, and they are
already starting to benefit people with disabilities. Maintaining
policies to encourage safety-conscious testing and deployment of self-
driving vehicles will benefit all Americans, and especially those with
disabilities.
Second, computer vision and image interpretation systems have the
potential to help those with visual disabilities process information
about their surroundings. These systems are demonstrating an increasing
capacity to identify specific objects and people in complex scenes, and
to model and predict what might happen next, such as warning of
potential dangers.
Third, AI can help to identify barriers to accessibility. For
example, Project Sidewalk at the University of Maryland combines
crowdsourced data collection with AI techniques to build a database of
curb, ramp, and sidewalk locations, and analyze it to identify
accessibility problems. This can help city planners and property owners
recognize accessibility failures.
Question 2. How will manufacturers and developers work to perfect
this technology so that it can truly be a reliable tool for these
individuals?
Answer. As with any new, complex technology, careful testing is
needed to understand the implications of using a system. Such testing
must be done in a realistic environment and must involve the community
of potential users.
The best design practices are user-centered, meaning that the
potential user community for a product is involved throughout the
design process, from initial concept exploration through final testing.
This is especially important if the designer might experience the world
differently than the user community.
Question 3. What more can Congress to do assist with these efforts?
Answer. Three significant things that Congress can do are (1)
provide funding for research on applications of AI for use by people
with disabilities; (2) work with agencies to ensure they are giving
proper attention to these issues and the interests of people with
disabilities; and (3) highlight the need for work in this area and
highlight the successes of those already working in the area.
Question 4. [citations omitted] As we see machine learning and AI
increasingly embedded in products and services that we rely on, there
are numerous cases of these algorithms falling short of consumer
expectations. For example, Google and Facebook both promoted fraudulent
news stories in the immediate wake of the Las Vegas Shooting because of
their algorithms. YouTube Kids is a service designed for children, and
marketed as containing videos that are suitable for very young
children. In November, YouTube Kids promoted inappropriate content due
to algorithms. While the use of machine learning and AI holds limitless
positive potential, at the current point, it faces challenges where we
should not risk getting it wrong. Should there be any formal or
informal guidelines in place for what tasks are suitable to be done by
algorithms, and which are still too important or sensitive to turn
over; and what more can be done to ensure better and more accurate
algorithms are used as you work to better develop this technology?
Answer. In considering a switch from human to AI based decision
making, we should not demand perfection of the AI system. The
alternative to AI is often to rely on human judgment, which is also
prone to bias and mistakes. Instead of demanding perfection of the AI
system, an organization needs to understand the potential consequences
of adopting the AI system well enough to conclude with justified
confidence that switching to an automated system is an improvement and
on balance the effects are benign.
There are also considerations of scale. AI can operate at larger
scale (that is, on larger number amounts of data or more decisions per
second) than any human organization can hope to achieve. As a result,
in some cases the choice in designing a function is not between using
AI and using a human, but rather between using AI and not providing the
function at all. The tremendous value provided by many of today's
information, communication, and publishing tools relies at least in
part on the use of AI.
That said, the potential risks of AI-based systems must be
considered and addressed. It is too early to establish formal
guidelines, because not enough is known about how best to address these
problems. Informal guidelines are needed, and the industry and other
stakeholders should be encouraged to develop them collaboratively.
Multi-stakeholder groups such as the Partnership on AI may be useful
venues for these discussions. The guidelines, best practices, and
technical tools to address these problems will evolve with time.
Switching a process based on human decision-making to one based on
AI can have unpredictable consequences, so experimentation is needed in
a safe environment to adequately understand the implications of such a
change before it is made. Organizations can be more transparent by
publishing information about what kinds of testing and analysis were
done in preparation for the introduction of AI into an existing
process, thereby enabling stakeholders to understand why a change was
made and query the organization if concerns remain.
In many cases, an organization will have valid reasons, such as
trade secrets or user privacy, to refrain from publishing the full
details of how a system works. This does not preclude the organization
from publishing information about how it tested the system and
evaluated the pros and cons of adopting it.
Question 5. [citations omitted] Machine learning and AI hold great
promise for assisting us in preventing cybersecurity attacks. According
to an IBM survey of Federal IT managers, 90 percent believe that
artificial intelligence could help the Federal Government defend
against real-world cyber-attacks. 87 percent think AI will improve the
efficiency of their cybersecurity workforce. While this is promising,
the Federal Government currently faces a shortage of qualified
cybersecurity employees, and to make matters worse, the pipeline of
students studying these topics is not sufficient to meet our needs. A
recent GAO report found that Federal agencies have trouble identifying
skills gaps, recruiting and retaining qualified staff, and lose out on
candidate due to Federal hiring processes. The George Washington
University Center for Cyber & Homeland Security recently released a
report titled ``Trends in Technology and Digital Security'' which
stated:
``Traditional security operations centers are mostly staffed
with tier one analysts staring at screens, looking for unusual
events or detections of malicious activity. This activity is
similar to physical security personnel monitoring video cameras
for intruders. It is tedious for humans, but it is a problem
really well-suited to machine learning.''
What effect will machine learning and AI will have on
cybersecurity; and how do you think the Federal Government can best
leverage the benefits offered by machine learning and AI to address our
cybersecurity workforce shortage?
Answer. As the GWU report suggests, cybersecurity tasks of a
routine nature can be automated, thereby reducing the need for human
operators do to lower-level work. This can free up workers to
concentrate on higher-level tasks requiring more skill and judgment,
and can help to mitigate the Federal Government's cybersecurity
personnel shortage.
Notwithstanding these opportunities, the Federal Government will
continue to face challenges in recruiting and retaining the best
technology talent. Many proposals exist to address these challenges by
improving hiring authorities, pay scales, and working conditions for
Federal technology workers, and by instituting or expanding training
and scholarship-for-service programs.
Question 6. Mr. Felten, as we heard you recently served as the
deputy U.S. Chief Technology Officer at the White Office of Science and
Technology Policy. One of the projects you worked on in that office was
a major report on Artificial Intelligence. That report was one of the
many important projects taken on by the Office of Science and
Technology Policy in recent years. And it's extremely disappointing
that President Trump has failed to nominate leaders for that office,
now more than ten months into his presidency. That's the longest a
president has gone without a science advisor since the Office of
Science and Technology Policy was established in law in 1976. I've led
two letters to President Trump urging him to nominate well-qualified
experts to lead this office, but so far we have seen nothing from this
administration. As a former leader at the Office of Science and
Technology Policy, could you please explain why this office is
important, and what kinds of qualities you look for in good nominees
for this office?
Answer. OSTP's importance derives from the central role of science
and technology in nearly every area of policy. In major policy areas
such as national security and defense, transportation, education, and
the economy, technology is critical to the most important challenges
and opportunities. Making the best decisions in these areas requires
input from and dialog with the technical community. Congress assigned
that role within the White House to OSTP.
AI is just one of the areas of interest for OSTP, but it connects
to many important policy questions. What should DoD's policy be on
autonomous weapons systems, and what position should the United States
take in international talks about such weapons? How will AI-driven
automation affect the job market, and how can American schoolchildren
and adults be educated and trained for the future workplace? What needs
to be done to improve highway safety as automated vehicles become
practical? How can American farmers, journalists, and businesses be
freed to use drones, while strengthening our defenses against potential
terrorist uses of the same technology? How will changes in information
technology affect the mission of the Intelligence Community, and what
kinds of people and capabilities will the IC need in the future? How
will cybersecurity concerns affect all of these goals? Each of these
questions can be better answered with the help of technical advisors
who have deep domain knowledge, connections to the relevant technical
communities, and a seat at the policy table.
A successful OSTP Director will be a trusted advisor to the
President and the President's senior advisors, a liaison to departments
and agencies on science and technology issues, and an ambassador to
scientific and technical communities in the United States and around
the world.
A candidate for OSTP Director should be a highly respected member
of the scientific/technical community, with a reputation for technical
knowledge and policy judgment. The candidate should be able to work
successfully across disciplines, acquiring knowledge and providing
advice across many subject areas with appropriate staff support. They
should be able to work successfully within the unusual administrative
and legal environment of the White House, and they should be able to
recruit, motivate, and lead a team of highly-skilled domain experts and
policy advisors.
Because the subject matter of science and technology is so
extensive, and the United States is blessed with leading experts in so
many specialties, no one person can hope to have the knowledge,
experience, and connections needed to provide advice in all technical
areas. A successful OSTP Director will recruit a team of topic-area
advisors who can provide context and guidance in specific areas and can
expand OSTP's ``surface area'' in coordinating with agencies, outside
experts, and the public.
[all]
| MEMBERNAME | BIOGUIDEID | GPOID | CHAMBER | PARTY | ROLE | STATE | CONGRESS | AUTHORITYID |
|---|---|---|---|---|---|---|---|---|
| Wicker, Roger F. | W000437 | 8263 | S | R | COMMMEMBER | MS | 115 | 1226 |
| Blunt, Roy | B000575 | 8313 | S | R | COMMMEMBER | MO | 115 | 1464 |
| Moran, Jerry | M000934 | 8307 | S | R | COMMMEMBER | KS | 115 | 1507 |
| Thune, John | T000250 | 8257 | S | R | COMMMEMBER | SD | 115 | 1534 |
| Baldwin, Tammy | B001230 | 8215 | S | D | COMMMEMBER | WI | 115 | 1558 |
| Udall, Tom | U000039 | 8260 | S | D | COMMMEMBER | NM | 115 | 1567 |
| Capito, Shelley Moore | C001047 | 8223 | S | R | COMMMEMBER | WV | 115 | 1676 |
| Capito, Shelley Moore | C001047 | 8223 | S | R | COMMMEMBER | WV | 115 | 1676 |
| Cantwell, Maria | C000127 | 8288 | S | D | COMMMEMBER | WA | 115 | 172 |
| Klobuchar, Amy | K000367 | 8249 | S | D | COMMMEMBER | MN | 115 | 1826 |
| Heller, Dean | H001041 | 8060 | S | R | COMMMEMBER | NV | 115 | 1863 |
| Peters, Gary C. | P000595 | 7994 | S | D | COMMMEMBER | MI | 115 | 1929 |
| Gardner, Cory | G000562 | 7862 | S | R | COMMMEMBER | CO | 115 | 1998 |
| Young, Todd | Y000064 | 7948 | S | R | COMMMEMBER | IN | 115 | 2019 |
| Blumenthal, Richard | B001277 | 8332 | S | D | COMMMEMBER | CT | 115 | 2076 |
| Lee, Mike | L000577 | 8303 | S | R | COMMMEMBER | UT | 115 | 2080 |
| Johnson, Ron | J000293 | 8355 | S | R | COMMMEMBER | WI | 115 | 2086 |
| Duckworth, Tammy | D000622 | S | D | COMMMEMBER | IL | 115 | 2123 | |
| Schatz, Brian | S001194 | S | D | COMMMEMBER | HI | 115 | 2173 | |
| Cruz, Ted | C001098 | S | R | COMMMEMBER | TX | 115 | 2175 | |
| Fischer, Deb | F000463 | S | R | COMMMEMBER | NE | 115 | 2179 | |
| Booker, Cory A. | B001288 | S | D | COMMMEMBER | NJ | 115 | 2194 | |
| Sullivan, Dan | S001198 | S | R | COMMMEMBER | AK | 115 | 2290 | |
| Cortez Masto, Catherine | C001113 | S | D | COMMMEMBER | NV | 115 | 2299 | |
| Hassan, Margaret Wood | H001076 | S | D | COMMMEMBER | NH | 115 | 2302 | |
| Inhofe, James M. | I000024 | 8322 | S | R | COMMMEMBER | OK | 115 | 583 |
| Markey, Edward J. | M000133 | 7972 | S | D | COMMMEMBER | MA | 115 | 735 |
| Nelson, Bill | N000032 | 8236 | S | D | COMMMEMBER | FL | 115 | 859 |
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