[House Hearing, 116 Congress]
[From the U.S. Government Publishing Office]
ARTIFICIAL INTELLIGENCE:
SOCIETAL AND ETHICAL IMPLICATIONS
=======================================================================
HEARING
BEFORE THE
COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY
HOUSE OF REPRESENTATIVES
ONE HUNDRED SIXTEENTH CONGRESS
FIRST SESSION
__________
JUNE 26, 2019
__________
Serial No. 116-32
__________
Printed for the use of the Committee on Science, Space, and Technology
[GRAPHIC NOT AVAILABLE IN TIFF FORMAT]
Available via the World Wide Web: http://science.house.gov
__________
U.S. GOVERNMENT PUBLISHING OFFICE
36-796PDF WASHINGTON : 2019
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COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY
HON. EDDIE BERNICE JOHNSON, Texas, Chairwoman
ZOE LOFGREN, California FRANK D. LUCAS, Oklahoma,
DANIEL LIPINSKI, Illinois Ranking Member
SUZANNE BONAMICI, Oregon MO BROOKS, Alabama
AMI BERA, California, BILL POSEY, Florida
Vice Chair RANDY WEBER, Texas
CONOR LAMB, Pennsylvania BRIAN BABIN, Texas
LIZZIE FLETCHER, Texas ANDY BIGGS, Arizona
HALEY STEVENS, Michigan ROGER MARSHALL, Kansas
KENDRA HORN, Oklahoma RALPH NORMAN, South Carolina
MIKIE SHERRILL, New Jersey MICHAEL CLOUD, Texas
BRAD SHERMAN, California TROY BALDERSON, Ohio
STEVE COHEN, Tennessee PETE OLSON, Texas
JERRY McNERNEY, California ANTHONY GONZALEZ, Ohio
ED PERLMUTTER, Colorado MICHAEL WALTZ, Florida
PAUL TONKO, New York JIM BAIRD, Indiana
BILL FOSTER, Illinois JAIME HERRERA BEUTLER, Washington
DON BEYER, Virginia JENNIFFER GONZALEZ-COLON, Puerto
CHARLIE CRIST, Florida Rico
SEAN CASTEN, Illinois VACANCY
KATIE HILL, California
BEN McADAMS, Utah
JENNIFER WEXTON, Virginia
C O N T E N T S
June 26, 2019
Page
Hearing Charter.................................................. 2
Opening Statements
Statement by Representative Eddie Bernice Johnson, Chairwoman,
Committee on Science, Space, and Technology, U.S. House of
Representatives................................................ 8
Written statement............................................ 9
Statement by Representative Jim Baird, Committee on Science,
Space, and Technology, U.S. House of Representatives........... 9
Written statement............................................ 11
Written statement by Representative Frank Lucas, Ranking Member,
Committee on Science, Space, and Technology, U.S. House of
Representatives................................................ 11
Witnesses:
Ms. Meredith Whittaker, Co-Founder, AI Now Institute, New York
University
Oral Statement............................................... 13
Written Statement............................................ 16
Mr. Jack Clark, Policy Director, OpenAI
Oral Statement............................................... 32
Written Statement............................................ 34
Mx. Joy Buolamwini, Founder, Algorithmic Justice League
Oral Statement............................................... 45
Written Statement............................................ 47
Dr. Georgia Tourassi, Director, Oak Ridge National Lab-Health
Data Sciences Institute
Oral Statement............................................... 74
Written Statement............................................ 76
Discussion....................................................... 92
Appendix I: Answers to Post-Hearing Questions
Ms. Meredith Whittaker, Co-Founder, AI Now Institute, New York
University..................................................... 120
Mr. Jack Clark, Policy Director, OpenAI.......................... 123
Mx. Joy Buolamwini, Founder, Algorithmic Justice League.......... 128
Dr. Georgia Tourassi, Director, Oak Ridge National Lab-Health
Data Sciences Institute........................................ 135
Appendix II: Additional Material for the Record
H. Res. 153 submitted by Representative Haley Stevens,
Chairwoman, Subcommittee on Research and Technology, Committee
on Science, Space, and Technology, U.S. House of
Representatives................................................ 140
ARTIFICIAL INTELLIGENCE:.
SOCIETAL AND ETHICAL IMPLICATIONS
----------
WEDNESDAY, JUNE 26, 2019
House of Representatives,
Committee on Science, Space, and Technology,
Washington, D.C.
The Committee met, pursuant to notice, at 10 a.m., in room
2318 of the Rayburn House Office Building, Hon. Eddie Bernice
Johnson [Chairwoman of the Committee] presiding.
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairwoman Johnson. The hearing will come to order. Without
objection, the Chair is authorized to declare recess at any
time.
Good morning, and welcome to our distinguished panel of
witnesses. We are here today to learn about the societal
impacts and ethical implications of a technology that is
rapidly changing our lives, namely, artificial intelligence.
From friendly robot companions to hostile terminators,
artificial intelligence (AI) has appeared in films and sparked
our imagination for many decades.
Today, it is no longer a futuristic idea, at least not
artificial intelligence designed for a specific task. Recent
advances in computing power and increases in data production
and collection have enabled artificial-intelligence-driven
technology to be used in a growing number of sectors and
applications, including in ways we may not realize. It is
routinely used to personalize advertisements when we browse the
internet. It is also being used to determine who gets hired for
a job or what kinds of student essays deserve a higher score.
The artificial intelligence systems can be a powerful tool
for good, but they also carry risk. The systems have been shown
to exhibit gender discrimination when displaying job ads,
racial discrimination in predictive policing, and socioeconomic
discrimination when selecting zip codes to offer commercial
products or services.
The systems do not have an agenda, but the humans behind
the algorithms can unwittingly introduce their personal biases
and perspectives into the design and use of artificial
intelligence. The algorithms are then trained with data that is
biased in ways both known and unknown. In addition to resulting
in discriminatory decisionmaking, biases in design and training
of algorithms can also cause artificial intelligence to fail in
other ways, for example, performing worse than clinicians in
medical diagnostics. We know that these risks exist. What we do
not fully understand is how to mitigate them.
We are also struggling with how to protect society against
intended misuse and abuse. There has been a proliferation of
general artificial intelligence ethics principles by companies
and nations alike. The United States recently endorsed an
international set of principles for the responsible
development. However, the hard work is in the translation of
these principles into concrete, effective action. Ethics must
be integrated into the earliest stages of the artificial
intelligence research and education, and continue to be
prioritized at every stage of design and deployment.
Federal agencies have been investing in this technology for
years. The White House recently issued an executive order on
Maintaining American Leadership in artificial intelligence and
updated the 2016 National Artificial Intelligence R&D Strategic
Plan. These are important steps. However, I also have concerns.
First, to actually achieve leadership, we need to be willing to
invest. Second, while few individual agencies are making ethics
a priority, the Administration's executive order and strategic
plan fall short in that regard. When mentioning it at all, they
approach ethics as an add-on rather than an integral component
of all artificial intelligence R&D (research and development).
From improving healthcare, transportation, and education,
to helping to solve poverty and improving climate resilience,
artificial intelligence has vast potential to advance the
public good. However, this is a technology that will transcend
national boundaries, and if the U.S. does not address the
ethics seriously and thoughtfully, we will lose the opportunity
to become a leader in setting the international norms and
standards in the coming decades. Leadership is not just about
advancing the technology; it is about advancing it responsibly.
I look forward to hearing the insights and recommendation
from today's expert panel on how the United States can lead in
the ethical development of artificial intelligence.
[The prepared statement of Chairwoman Johnson follows:]
Good morning, and welcome to our distinguished panel of
witnesses.
We are here today to learn about the societal impacts and
ethical implications of a technology that is rapidly changing
our lives, namely, Artificial intelligence.
From friendly robot companions to hostile terminators, AI
has appeared in films and sparked our imagination for many
decades. Today, AI is no longer a futuristic idea, at least not
AI designed for specific tasks. Recent advances in computing
power and increases in data production and collection have
enabled AI-driven technology to be used in a growing number of
sectors and applications, including in ways we may not realize.
AI is routinely used to personalize advertisements when we
browse the internet. It is also being used to determine who
gets hired for a job or what kinds of student essays deserve a
higher score.
AI systems can be a powerful tool for good, but they also
carry risks. AI systems have been shown to exhibit gender
discrimination when displaying job ads, racial discrimination
in predictive policing, and socioeconomic discrimination when
selecting which zip codes to offer commercial products or
services.
The AI systems do not have an agenda, but the humans behind
the algorithms can unwittingly introduce their personal biases
and perspectives into the design and use of AI. The algorithms
are then trained with data that is biased in ways both known
and unknown. In addition to resulting in discriminatory
decision-making, biases in the design and training of
algorithms can also cause AI to fail in other ways, for example
performing worse than clinicians in medical diagnostics.
We know that these risks exist. What we do not fully
understand is how to mitigate them. We are also struggling with
how to protect society against intended misuse and abuse of AI.
There has been a proliferation of general AI ethics principles
by companies and nations alike. The United States recently
endorsed an international set of principles for the responsible
development of AI. However, the hard work is in the translation
of these principles into concrete, effective action. Ethics
must be integrated at the earliest stages of AI research and
education, and continue to be prioritized at every stage of
design and deployment.
Federal agencies have been investing in AI technology for
years. The White House recently issued an executive order on
Maintaining American Leadership in AI and updated the 2016
National Artificial Intelligence R&D Strategic Plan. These are
important steps. However, I also have concerns. First, to
actually achieve leadership, we need to be willing to invest.
Second, while a few individual agencies are making ethics a
priority, the Administration's executive order and strategic
plan fall short in that regard. When mentioning it at all, they
approach ethics as an add-on rather than an integral component
of all AI R&D.
From improving healthcare, transportation, and education,
to helping to solve poverty and improving climate resilience,
AI has vast potential to advance the public good. However, this
is a technology that will transcend national boundaries, and if
the U.S. does not address AI ethics seriously and thoughtfully,
we will lose the opportunity to become a leader in setting the
international norms and standards for AI in the coming decades.
Leadership is not just about advancing the technology, it's
about advancing it responsibly.
I look forward to hearing the insights and recommendations
from today's expert panel on how the United States can lead in
the ethical development of AI.
Chairwoman Johnson. I now recognize Mr. Baird for his
opening statement.
Mr. Baird. Thank you, Chairwoman Johnson, for holding this
hearing today on the societal and ethical implications of
artificial intelligence, AI.
In the first half of the 20th century, the concept of
artificial intelligence was the stuff of science fiction.
Today, it's a reality. Since the term AI was first coined in
the 1950s, we have made huge advances in the field of
artificial narrow intelligence. Narrow AI systems can perform a
single task like providing directions through Siri or giving
you weather forecasts. This technology now touches every part
of our lives and every sector of the economy.
Driving the growth of AI is the availability of big data.
Private companies and government have collected large datasets,
which, combined with advanced computing power, provide the raw
material for dramatically improved machine-learning approaches
and algorithms. How this data is collected, used, stored,
secured is at the heart of the ethical and policy debate over
the use of AI.
AI has already delivered significant benefits for U.S.
economic prosperity and national security, but it has also
demonstrated a number of vulnerabilities, including the
potential to reinforce existing social issues and economic
imbalances.
As we continue to lead the world in advanced computing
research, a thorough examination of potential bias, ethics, and
reliability challenges of AI is critical to maintaining our
leadership in technology. The United States must remain the
leader in AI, or we risk letting other countries who don't
share our values drive the standards for this technology. To
remain the leader in AI, I also believe Americans must
understand and trust how AI technologies will use their data.
The Trump Administration announced earlier this year an
executive order on ``Maintaining American Leadership in
Artificial Intelligence.'' Last week, the Administration's
Select Committee on AI released a report that identifies its
priorities for federally funded AI research. I'm glad that the
Administration is making AI research a priority. This is an
effort that is going to require cooperation between industry,
academia, and Federal agencies. In government, these efforts
will be led by agencies under the jurisdiction of this
Committee, including NIST (National Institute of Standards and
Technology), NSF (National Science Foundation), and DOE
(Department of Energy).
We will learn more about one of those research efforts from
one of our witnesses today, Dr. Georgia Tourassi, the Founding
Director of the Health Data Sciences Institute at Oak Ridge
National Laboratory. Dr. Tourassi's research focuses on
deploying AI to provide diagnoses and treatment for cancer. Her
project is a good example of how cross-agency collaboration and
government data can responsibly drive innovation for public
good. I look forward to hearing more about her research.
Over the next few months, this Committee will be working
toward bipartisan legislation to support a national strategy on
artificial intelligence. The challenges we must address are how
industry, academia, and the government can best work together
on AI challenges, including ethical and societal questions, and
what role the Federal Government should play in supporting
industry as it drives innovation.
I want to thank our accomplished panel of witnesses and
their testimony today, and I look forward to hearing what role
Congress should play in facilitating this conversation.
[The prepared statement of Mr. Baird follows:]
Chairwoman Johnson, thank you for holding today's hearing
on the societal and ethical implications of artificial
intelligence (AI).
In the first half of the 20th century, the concept of
artificial intelligence was the stuff of science fiction. Today
it is reality.
Since the term AI was first coined in the 1950s, we have
made huge advances in the field of artificial narrow
intelligence.
Narrow AI systems can perform a single task like providing
directions through Siri or giving you weather forecasts. This
technology now touches every part of our lives and every sector
of the economy.
Driving the growth of AI is the availability of big data.
Private companies and government have collected large data
sets, which, combined with advanced computing power, provide
the raw material for dramatically improved machine learning
approaches and algorithms.
How this data is collected, used, stored, and secured is at
the heart of the ethical and policy debate over the use of AI.
AI has already delivered significant benefits for U.S.
economic prosperity and national security.
But it has also demonstrated a number of vulnerabilities,
including the potential to reinforce existing social issues and
economic imbalances.
As we continue to lead the world in advanced computing
research, a thorough examination of potential bias, ethics, and
reliability challenges of AI is critical to maintaining our
leadership in this technology.
The United States must remain the leader in AI, or we risk
letting other countries who don't share our values drive the
standards for this technology.
To remain the leader AI, I believe Americans must also
understand and trust how AI technologies will use their data.
The Trump Administration announced earlier this year an
Executive Order on "Maintaining American Leadership in
Artificial Intelligence."
Last week the Administration's Select Committee on AI
released a report that identifies its priorities for federally
funded AI research.
I am glad that the Administration is making AI research a
priority.
This is an effort that is going to require cooperation
between industry, academia and federal agencies.
In government, these efforts will be led by agencies under
the jurisdiction of this Committee, including NIST, NSF and
DOE.
We will learn more about one of those research efforts from
one of our witnesses today, Dr. Georgia Tourassi, the founding
Director of the Health Data Sciences Institute (HDSI) at Oak
Ridge National Laboratory. Dr. Tourassi's research focuses on
deploying AI to provide diagnoses and treatment of cancer.
Her project is a good example of how cross-agency
collaboration and government data can responsibly drive
innovation for public good. I look forward to hearing more
about her research.
Over the next few months, this Committee will be working
towards bipartisan legislation to support a national strategy
on Artificial Intelligence.
The challenges we must address are how industry, academia,
and the government can best work together on AI challenges,
including ethical and societal questions, and what role the
federal government should play in supporting industry as it
drives innovation.
I want to thank our accomplished panel of witnesses for
their testimony today and I look forward to hearing what role
Congress should play in facilitating this conversation.
Chairwoman Johnson. Thank you very much.
If there are Members who wish to submit additional opening
statements, your statements will be added to the record at this
point.
[The prepared statement of Mr. Lucas follows:]
Today, we will explore the various applications and
societal implications of Artificial Intelligence (AI), a
complex field of study where researchers train computers to
learn directly from information without being explicitly
programmed - like humans do.
Last Congress, this Committee held two hearings on this
topic - examining the concept of Artificial General
Intelligence (AGI) and discussing potential applications for AI
development through scientific machine learning, as well as the
cutting-edge basic research it can enable.
This morning we will review the types of AI technologies
being implemented all across the country and consider the most
appropriate way to develop fair and responsible guidelines for
their use.
From filtering your inbox for spam to protecting your
credit card from fraudulent activity, AI technologies are
already a part of our everyday lives. AI is integrated into
every major U.S. economic sector, including transportation,
health care, agriculture, finance, national defense, and space
exploration.
This influence will only expand. In 2016, the global AI
market was valued at over $4 billion and is expected to grow to
$169 billion by 2025. Additionally, there are estimates that AI
could add $15.7 trillion to global GDP by 2030.
Earlier this year, the Trump Administration announced a
plan for "Maintaining American Leadership in Artificial
Intelligence."
Last week, the Administration's Select Committee on
Artificial Intelligence released a report that identifies its
priorities for federally funded AI research. These include
developing effective methods for human-AI collaboration,
understanding and addressing the ethical, legal, and societal
implications of AI, ensuring the safety and security of AI
systems, and evaluating AI technologies through standards and
benchmarks.
Incorporating these priorities while driving innovation in
AI will require cooperation between industry, academia, and the
Federal government. These efforts will be led by agencies under
the jurisdiction of this Committee: the National Institute on
Standards and Technology (NIST), the National Science
Foundation (NSF), and the Department of Energy (DOE).
The AI Initiative specifically directs NIST to develop a
federal plan for the development of technical standards in
support of reliable, robust, and trustworthy AI technologies.
NIST plans to support the development of these standards by
building research infrastructure for AI data and standards
development and expanding ongoing research and measurement
science efforts to promote adoption of AI in the marketplace.
At the NSF, federal investments in AI span fundamental
research in machine learning, along with the security,
robustness, and explainability of AI systems. NSF also plays an
essential role in supporting diverse STEM education, which will
provide a foundation for the next generation AI workforce. NSF
also partners with U.S. industry coalitions to emphasize
fairness in AI, including a program on AI and Society which is
jointly supported by the Partnership on AI to Benefit People
and Society (PAI).
Finally, with its world-leading user facilities and
expertise in big data science, advanced algorithms, and high-
performance computing, DOE is uniquely equipped to fund robust
fundamental research in AI.
Dr. Georgia Tourassi, the founding Director of the Health
Data Sciences Institute (HDSI), joins us today from Oak Ridge
National Laboratory (ORNL) - a DOE Office of Science
Laboratory. Dr. Tourassi's research focuses on deploying AI to
provide diagnoses and treatment for cancer.
The future of scientific discovery includes the
incorporation of advanced data analysis techniques like AI.
With the next generation of supercomputers, including the
exascale computing systems that DOE is expected to field by
2021, American researchers will be able to explore even bigger
challenges using AI. They will have greater power, and even
more responsibility.
Technology experts and policymakers alike have argued that
without a broad national strategy for advancing AI, the U.S.
will lose its narrow global advantage. With increasing
international competition in AI and the immense potential for
these technologies to drive future technological development,
it's clear the time is right for the federal government to lead
these conversations about AI standards and guidelines.
I look forward to working with Chairwoman Johnson and the
members of the Committee over the next few months to develop
legislation that supports this national effort.
I want to thank our accomplished panel of witnesses for
their testimony today and I look forward to receiving their
input.
Chairwoman Johnson. At this time, I will introduce our
witnesses. Our first witness is Ms. Meredith Whittaker. Ms.
Whittaker is a distinguished research scientist at New York
University and Co-Founder and Co-Director of the AI Now
Institute, which is dedicated to researching the social
implications of artificial intelligence and related
technologies. She has over a decade of experience working in
the industry, leading product and engineering teams.
Our next witness is Mr. Jack Clark. Mr. Clark is the Policy
Director of OpenAI where his work focuses on AI policy and
strategy. He's also a Research Fellow at the Center for
Security and Emerging Technology at Georgetown University and a
member of the Center of the New American Security task force at
AI National Security. Mr. Clark also helps run the AI Index, an
initiative from the Stanford One Hundred Year Study on AI to
track AI progress.
After Mr. Clark is Mx. Joy Buolamwini, who is Founder of
the Algorithmic Justice League and serves on the Global Tech
Panel convened by the Vice President of the European Union to
advise leaders and technology executives on ways to reduce the
potential harms of AI. She is also a graduate researcher at MIT
where her research focuses on algorithmic bias and computer
version systems.
Our last witness, Dr. Georgia Tourassi. Dr. Tourassi is the
Founding Director of the Health and Data Sciences Institute and
Group Leader of Biomedical Sciences, Engineering, and Computing
at the Oak Ridge National Laboratory. Her research focuses on
artificial intelligence for biomedical applications and data-
driven biomedical discovery. Dr. Tourassi also serves on the
FDA (Food and Drug Administration) Advisory Committee and
Review Panel on Computer-aided Diagnosis Devices.
Our witnesses should know that you will have 5 minutes for
your spoken testimony. Your written testimony will be included
in the record for the hearing. When you all have completed your
spoken testimony, we will begin with a round of questions. Each
Member will have 5 minutes to question the panel.
We now will start with Ms. Whittaker.
TESTIMONY OF MEREDITH WHITTAKER,
CO-FOUNDER, AI NOW INSTITUTE,
NEW YORK UNIVERSITY
Ms. Whittaker. Chairwoman Johnson, Ranking Member Baird,
and Members of the Committee, thank you for inviting me to
speak today. My name is Meredith Whittaker, and I'm the Co-
Founder of the AI Now Institute at New York University. We're
the first university research institute dedicated to studying
the social implications of artificial intelligence and
algorithmic technologies.
The role of AI in our core social institutions is
expanding. AI is shaping access to resources and opportunity
both in government and in the private sector with profound
implications for hundreds of millions of Americans. These
systems are being used to judge who should be released on bail;
to automate disease diagnosis; to hire, monitor, and manage
workers; and to persistently track and surveil using facial
recognition. These are a few examples among hundreds. In short,
AI is quietly gaining power over our lives and institutions,
and at the same time AI systems are slipping farther away from
core democratic protections like due process and a right
refusal.
In light of this, it is urgent that Congress act to ensure
AI is accountable, fair, and just because this is not what is
happening right now. We at AI Now, along with many other
researchers, have documented the ways in which AI systems
encode bias, produce harm, and differ dramatically from many of
the marketing claims made by AI companies.
Voice-recognition hears masculine sounding voices better
than feminine voices. Facial recognition fails to see black
faces and transgendered faces. Automated hiring systems
discriminate against women candidates. Medical diagnostic
systems don't work for dark-skinned patients. And the list goes
on, revealing a persistent pattern of gender and race-based
discrimination, among other forms of identity.
But even when these systems do work as intended, they can
still cause harm. The application of 100 percent accurate AI to
monitor, track, and control vulnerable populations raises
fundamental issues of power, surveillance, and basic freedoms
in our democratic society. This reminds us that questions of
justice will not be solved simply by adjusting a technical
system.
Now, when regulators, researchers, and the public seek to
understand and remedy potential harms, they're faced with
structural barriers. This is because the AI industry is
profoundly concentrated, controlled by just a handful of
private tech companies who rely on corporate secrecy laws that
make independent testing and auditing nearly impossible.
This also means that much of what we do know about AI is
written by the marketing departments of these same companies.
They highlight hypothetical benevolent uses and remain silent
about the application of AI to fossil fuel extraction, weapons
development, mass surveillance, and the problems of bias and
error. Information about the darker side of AI comes largely
thanks to researchers, investigative journalists, and
whistleblowers.
These companies are also notoriously non-diverse. AI Now
conducted a year-long study of diversity in the AI industry,
and the results are bleak. To give an example of how bad it is,
in 2018 the share of women in computer science professions
dropped below 1960 levels. And this means that women, people of
color, gender minorities, and others are excluded from shaping
how AI systems function, and this contributes to bias.
Now, while the costs of such bias are borne by historically
marginalized people, the benefits of such systems, from profits
to efficiency, accrue primarily to those already in positions
of power. This points to problems that go well beyond the
technical. We must ask who benefits from AI, who is harmed, and
who gets to decide? This is a fundamental question of
democracy.
Now, in the face of mounting criticism, tech companies are
adopting ethical principles. These are a positive start, but
they don't substitute for meaningful public accountability.
Indeed, we've seen a lot of P.R., but we have no examples were
such ethical promises are backed by public enforcement.
Congress has a window to act, and the time is now. Powerful
AI systems are reshaping our social institution in way--
institutions in ways we're unable to measure and contest. These
systems are developed by a handful of private companies whose
market interests don't always align with the public good and
who shield themselves from accountability behind claims of
corporate secrecy. When we are able to examine these systems,
too often we find that they are biased in ways that replicate
historical patterns of discrimination. It is imperative that
lawmakers regulate to ensure that these systems are
accountable, accurate, contestable, and that those most at risk
of harm have a say in how and whether they are used.
So in pursuit of this goal, AI Now recommends that
lawmakers, first, require algorithmic impact assessments in
both public and private sectors before AI systems are acquired
and used; second, require technology companies to waive trade
secrecy and other legal claims that hinder oversight and
accountability mechanisms; third, require public disclosure of
AI systems involved in any decisions about consumers; and
fourth, enhance whistleblower protections and protections for
conscientious objectors within technology companies.
Thank you, and I welcome your questions.
[The prepared statement of Ms. Whittaker follows:]
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairwoman Johnson. Thank you. Mr. Jack Clark.
TESTIMONY OF JACK CLARK,
POLICY DIRECTOR, OPENAI
Mr. Clark. Chairwoman Johnson, Ranking Member Baird, and
Committee Members, thank you for inviting me today. I'm the
Policy Director for OpenAI, a technical research lab based in
San Francisco.
I think the reason why we're here is that AI systems have
become--and I'm using air quotes--good enough to be deployed
widely in society, but lots of the problems that we're going to
be talking about are because of ``good enough'' AI. We should
ask, ``good enough for who?'', and we should also ask ``good
enough at what?''
So to give you some context, recent advances in AI have let
us write software that can interpret the contents of an image,
understand wave forms in audio, or classify movements in video,
and more. At the same time, we're seeing the resources applied
to AI development grow significantly. One analysis performed by
OpenAI found that the amount of computing power used to train
certain AI systems had increased by more than 300,000 times in
the last 6 years, correlating to significant economic
investments on the part of primarily industry in developing
these systems.
But though these systems have become better at doing the
tasks we set for them, they display problems in deployment. And
these problems are typically a consequence of people failing to
give the systems the right objectives or giving them the right
training data. Some of these problems include popular image
recognition systems that have been shown to accurately classify
products from rich countries and fail to classify products from
poor countries, voice recognition systems that perform
extremely badly when dealing with people who are speaking in
English that is heavily accented, or commercially available
facial recognition systems that consistently misclassify or
fail to classify people with darker skin tones.
So why these issues arise is because many modern machine
learning systems automate tasks that require people to make
value judgments. And so when people make value judgments, they
encode their values into the system, whether that's the value
of who's got to be in the dataset or what the task is that it's
solving. And because, as my co-panelists have mentioned, these
people are not from a particularly diverse background, you can
also expect problems to come from these people selecting values
that apply to many people.
These systems can also fail as a consequence of technical
issues, so image classification systems can be tricked using
things known as adversarial examples to consistently
misclassify things they see in an image. More confusingly and
worryingly, we found that you can break these systems simply by
putting something in an image that they don't expect to see.
And one memorable study did this by placing an elephant in a
room, which would cause the image recognition system to
misclassify other things in that room even though it wasn't
being asked to look at it. So that gives you a sense of how
brittle these systems can be if they're applied in the context
which they don't expect.
I think, though, that these technical issues are in a sense
going to be easier to deal with than the social issues. The
questions of how these systems are deployed, who is deploying
them, and who they're being deployed to help or surveil are the
questions that I think we should focus on here. And to that end
I have a few suggestions for things that I think government,
industry, and academia can do to increase the safety of these
systems.
First, I think we need additional transparency. And what I
mean by transparency is government should convene academia and
industry to create better tools and tests and assessment
schemes such as the, you know, algorithmic impact assessment or
work like adding a label to datasets which are widely used so
that people know what they're using and have tools to evaluate
their performance.
Second, government should invest in its own measurement
assessments and benchmarking schemes potentially by agencies
such as NIST. The reason we should do this is that, as we
develop these systems for assessing things like bias, we would
probably want to roll them into the civil sector and have a
government agency perform regular testing in partnership with
academia to give the American people a sense of what these
systems are good at, what they're bad at, and, most crucially,
who they're failing.
Finally, I think government should increase funding for
interdisciplinary research, a common problem is these systems
are developed by a small number of people from homogenous
backgrounds, and they can also be studied in this way because
grants are not particularly friendly to large-scale
interdisciplinary research projects. So we should think about
ways we can study AI that brings together computer scientists,
lawyers, social scientists, philosophers, security experts, and
more, not just 20 computer science professionals and a single
lawyer, which is some people's definition of interdisciplinary
research.
So, in conclusion, I think we have a huge amount of work to
do, but I think that there's real work that can be done today
that can let us develop better systems for oversight and
awareness of this technology. Thank you very much.
[The prepared statement of Mr. Clark follows:]
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairwoman Johnson. Thank you very much. Mx. Joy
Buolamwini.
TESTIMONY OF JOY BUOLAMWINI,
FOUNDER, ALGORITHMIC JUSTICE LEAGUE
Mx. Buolamwini. Thank you, Chairwoman Johnson, Ranking
Member Baird, and fellow Committee Members, for the opportunity
to testify. I'm an algorithmic bias researcher based at MIT.
I've conducted studies showing some of the largest recorded
racial skin type and gender biases in systems sold by IBM,
Microsoft, and Amazon. This research exposes limitations of AI
systems that are infiltrating our lives, determining who gets
hired or fired, and even who's targeted by the police.
Research continues to remind us that sexism, racism,
ableism, and other intersecting forms of discrimination can be
amplified by AI. Harms can arise unintended. The interest in
self-driving cars is in part motivated by the promise they will
reduce the more than 35,000 annual vehicle fatalities. A June
2019 study showed that for the task of pedestrian tracking,
children were less likely to be detected than adults. This
finding motivates concerns that children could be at higher
risk for being hit by self-driving cars. When AI-enabled
technologies are presented as lifesavers, we must ask which
lives will matter.
In healthcare, researchers are exploring how to apply AI-
enabled facial analysis systems to detect pain and monitor
disease. An investigation of algorithmic bias for clinical
populations showed these AI systems demonstrated poor
performance on older adults with dementia. Age and ability
should not impede quality of medical treatment, but without
care, AI and health can worsen patient outcomes.
Behavior-based discrimination can also occur, as we see
with the use of AI to analyze social media content. The U.S.
Government is monitoring social media activities to inform
immigration decisions despite a Brennan Center report and a
USCIS (U.S. Citizenship and Immigration Services) study
detailing how such methods are largely ineffective for
determining threats to public safety or national security.
Immigrants and people in low-income families are especially at
risk for having to expose their most sensitive information, as
is in the case when AI systems are used to determine access to
government services.
Broadly speaking, AI harms can be traced first to
privileged ignorance. The majority of researchers,
practitioners, and educators in the field are shielded from the
harms of AI, leading to undervaluation, de-prioritization, and
ignorance of problems, along with decontextualized solutions.
Second, negligent industry and academic norms, there's an
ongoing lack of transparency and nuanced evaluations of the
limitations of AI.
And third, and overreliance on biased data that reflects
structural inequalities coupled with a belief in techno-
solutionism. For example, studies of automated risk assessment
tools used in the criminal justice system show continued racial
bias in the penal system, which cannot be remedied with
technical fixes.
We must do better. At the very least, government-funded
research on human-centered AI should require the documentation
of both included and excluded demographic groups.
Finally, I urge Congress to ensure funding without conflict
of interest is available for AI research in the public
interest. After co-authoring a peer-reviewed paper testing
gender and skin type bias in an Amazon product which revealed
error rates of 0 percent for white men and 31 percent for women
of color, I faced corporate hostility as a company Vice
President made false statements attempting to discredit my MIT
research. AI research that exposes harms which challenge
business interests need to be supported and protected.
In addition to addressing the Computer Fraud and Abuse Act,
which criminalizes certain forms of algorithmic biased
research, Congress can issue an AI accountability tax. A
revenue tax of just .5 percent on Google, Microsoft, Amazon,
Facebook, IBM, and Apple would provide more than $4 billion of
funding for AI research in the public interest and support
people who are impacted by AI harms.
Public opposition is already mounting against harmful use
of AI, as we see with the recent face recognition ban in San
Francisco and a proposal for a Massachusetts Statewide
moratorium. Moving forward, we must make sure that the future
of AI development, research, and education in the United States
is truly of the people, by the people, and for all the people,
not just the powerful and privileged. Thank you.
Next, I look forward to answering your questions.
[The prepared statement of Mx. Buolamwini follows:]
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairwoman Johnson. Thank you very much.
Dr. Georgia Tourassi.
TESTIMONY OF DR. GEORGIA TOURASSI,
DIRECTOR, HEALTH DATA SCIENCES INSTITUTE,
OAK RIDGE NATIONAL LABORATORY
Dr. Tourassi. Chairwoman Johnson, Ranking Member Baird, and
distinguished Members of the Committee, thank you for the
opportunity to appear before you today. My name is Georgia
Tourassi. I'm a Distinguished Scientist in the Computing and
Computational Sciences Directorate and the Director of the
Health Data Sciences Institute of the U.S. Department's Oak
Ridge National Laboratory in Oak Ridge, Tennessee. It is an
honor to provide this testimony on the role of the Department
of Energy and its national laboratories in spearheading
responsible use of Federal data assets for AI innovation in
healthcare.
The dramatic growth of AI is driven by big data, massive
compute power, and novel algorithms. The Oak Ridge National Lab
is equipped with exceptional resources in all three areas.
Through the Department of Energy's Strategic Partnership
Projects program, we are applying these resources to challenges
in healthcare.
Data scientists at Oak Ridge have developed AI solutions to
modernize the National Cancer Institute's surveillance program.
These solutions are being implemented across several cancer
registries where they are demonstrating high accuracy and
improved efficiency, making near real-time cancer incidents
reporting a reality.
In partnership with the Veterans Administration, the Oak
Ridge National Lab has brought its global leadership in
computing and big data to the task of hosting and analyzing the
VA's vast array of healthcare and genomic data. This
partnership brings together VA's data assets with DOE's world-
class high-performance computing assets and scientific
workforce to enable AI innovation and improve the health of our
veterans. These are examples that demonstrate what can be
achieved through a federally coordinated AI strategy.
But with the great promise of AI comes an even greater
responsibility. There are many ethical questions when applying
AI in medicine. I will focus on questions related to the ethics
of data and the ethics of AI development and deployment.
With respect to the ethics of data, the massive volumes of
health data must be carefully protected to preserve privacy
even as we extract valuable insights. We need secure digital
infrastructure that is sustainable and energy-efficient to
accommodate the ever-growing datasets and computational AI
needs. We also need to address the sensitive issues about data
ownership and data use as the line between research use and
commercial use is blurry.
With respect to the ethics of AI development and
deployment, we know that AI algorithms are not immune to low-
quality data or biased data. The DOE national laboratories,
working with other Federal agencies, could provide the secure
and capable computing environment for objective benchmarking
and quality control of sensitive datasets and AI algorithms
against community consensus metrics.
Because one size will not fit all, we need a federally
coordinated conversation involving not only the STEM (science,
technology, engineering, and mathematics) sciences but also
social sciences, economics, law, public policy stakeholders to
address the emerging domain-specific complexities of AI use.
Last, we must build an inclusive and diverse AI workforce
to deliver solutions that are beneficial to all. The Human
Genome Project included a program on the ethical, legal, and
social implications of genomic research that had a lasting
impact on how the entire community from basic researchers to
drug companies to medical workers used and handled genomic
data. The program could be a model for a similar effort to
realize the hope of AI in transforming health care.
The DOE national laboratories are uniquely equipped to
support a national strategy in AI research, development,
education, and stakeholder coordination that addresses the
security, societal, and ethical challenges of AI in health
care, particularly with respect to the Federal data assets.
Thank you again for the opportunity to testify. I welcome
your questions on this important topic.
[The prepared statement of Dr. Tourassi follows:]
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairwoman Johnson. Thank you very much. At this point, we
will begin our first round of questions, and the Chair
recognizes herself for 5 minutes.
My questions will be to all witnesses. This Committee has
led congressional discussions and action on quantum science,
engineering, biology, and many other emerging technologies over
the years. In thinking about societal implications and
governance, how is AI similar to, or different from, other
transformational technologies, and how should we be thinking
about it differently? We'll start with you, Ms. Whittaker.
Ms. Whittaker. Thank you, Chairwoman. I think there are
many similarities and differences. In the case of AI, as I
mentioned in my opening statement and in my written testimony,
what you see is a profoundly corporate set of technologies.
These are technologies that, because of the requirement to have
massive amounts of computational infrastructure and massive
amounts of data, aren't available for anyone with an interest
to develop or deploy.
When we talk about AI, we're generally talking about
systems that are deployed by the private sector in ways that
are calibrated ultimately to maximize revenue and profit. So we
need to look carefully at the interests that are driving the
production and deployment of AI, and put in place regulations
and checks to ensure that those interests don't override the
public good.
Chairwoman Johnson. Mr. Clark.
Mr. Clark. It's similar in the sense that it's a big deal
in the way that 5G or quantum computers are going to
revolutionize chunks of the economy. Maybe the difference is
that it's progressing much more rapidly than this technology
and it's also being deployed at scale much more rapidly. And I
think that the different nature of the pace and scale of
deployment means that we need additional attention here
relative to the other technologies that you've been discussing.
Mx. Buolamwini. I definitely would want to follow up on
scale particularly because even though very few companies tend
to dominate the field, the technologies that they deploy can be
used by many people around the world. So one example is a
company called Megvii that we audited that provides facial
analysis capabilities. And more than 100,000 developers use
that technology. So you have a case where a technology that is
developed by a small group of people can proliferate quite
widely and that biases can also compound very quickly.
Chairwoman Johnson. Yes.
Dr. Tourassi. So in the context of the panel I would like
to focus on the differences between AI and the technologies
that you outlined: Quantum computing and others. AI is not
simply about computers or about algorithms. It's about its
direct application and use by the humans. So it's fundamentally
a human endeavor compared to the other technological advances
that you outlined.
Chairwoman Johnson. Is it ever too early to start
integrating ethical thinking and considerations into all AI
research, education, or training, or how can the Federal
science agencies incentivize early integration of ethical
considerations in research and education at universities or
even at K through 12 level?
Ms. Whittaker. This is a wonderful question. As I mentioned
in my written testimony, I think it is never too early to
integrate these concerns, and I think we need to broaden the
field of AI research and AI development, as many of my co-
panelists have said, to include disciplines beyond the
technical. So we need to account for, as we say at AI Now, the
full stack supply chain accounting for the context in which AI
is going to be used, accounting for the experience of the
communities who are going to be classified and whose lives are
going to be shaped by the systems, and we need to develop
mechanisms to include these at every step of decisionmaking so
that we ensure in complex social contexts where these tools are
being used that they're safe and that the people most at risk
of harm are protected.
Chairwoman Johnson. Thank you.
Mr. Clark. Very briefly, I think NSF can best integrate
ethics into the aspect of grantmaking and also how you can kind
of gate for ethics on certain grant applications. And
additionally, we should put a huge emphasis on K through 12. I
think if you look at the pipeline of people in AI, they drop
out earlier than college, and so we should reach them before
then.
Mx. Buolamwini. We're already seeing initiatives where even
kids as young as 5 and 6 are being taught AI, and there's an
opportunity to also teach issues with bias and the need for
responsibility. And we're also starting to see competitions
that incentivize the creation of responsible AI curriculum.
Mozilla Foundation is conducting one of these competitions
right now at the undergraduate level.
We also need to look at ways of learning AI that are
outside of formal education and look at the different types of
online courses that are available for people who might not
enter the field in traditional ways and make sure that we're
also including ethical and responsible considerations in those
areas.
Chairwoman Johnson. OK. I'm over my time, but go ahead
briefly.
Dr. Tourassi. As I mentioned in my oral and written
testimony, the Human Genome Project represents an excellent
example of why and how the ethical, social, and legal
implications of AI need to be considered from the beginning,
not as an afterthought. Therefore, it should follow both of the
scientific realm and having dedicated workforce in that
particular space with stakeholders from several different
entities to certainly protect and remain vigilant in terms of
the scientific advances and the deployment implications of the
technology.
Chairwoman Johnson. Thank you very much. Mr. Baird.
Mr. Baird. Thank you, Madam Chair.
Dr. Tourassi, in this Congress the House Science Committee
has introduced H.R. 617 the Department of Energy Veterans
Health Initiative Act, a bill which I am also a cosponsor. I'm
also a Vietnam veteran. And that bill directs the DOE to
establish a research program in AI and high-performance
computing that's focused on supporting the VA by helping solve
big data challenges associated with veterans' health care. In
your prepared testimony you highlighted Oak Ridge National
Laboratory's work with the joint DOE-VA Million Veterans
Program or MVP-CHAMPION (Million Veterans Program Computational
Health Analytics for Medical Precision to Improve Outcomes
Now).
So my question is from your perspective what was the
collaboration process like with the VA?
Dr. Tourassi. From the scientific perspective, it has been
a very interesting and fruitful collaboration. Speaking as a
scientist who spent a couple of decades in clinical academia
before I moved to the Department of Energy, I would say that
there is a cultural shift between the two communities. The
clinical community will always be focused on translational
value and short-term gains when the basic scientific community
will be focused on not short-term solutions but disruptive
solutions with sustainable value.
In that respect, these are two complementary forces, and I
applaud the synergy between basic sciences and applied
sciences. It is a relay. Without an important application, we
cannot drive meaningfully basic science and vice versa.
Mr. Baird. Thank you. And continuing on, what do you feel
we can accomplish by managing that large database, and what do
you think will help in the----
Dr. Tourassi. This answer applies not only to the
collaboration with the Veterans Administration but in general
in the healthcare space. Health care is one of the areas that
will be most impacted by artificial intelligence in the 21st
century. We have a lot of challenges that do have digital
solutions that are compute data-intensive and, by extension,
energy security and energy consumption is an issue.
In that respect the collaboration between the DOE national
laboratories with the exceptional resources and expertise they
have in big data management, secure data management, advanced
analytics, and with high-performance computing can certainly
spearhead the transformation and enable the development and
deployment of tools that will have lasting value in the
population.
Mr. Baird. So thank you. And continuing on, in your opinion
who should be responsible for developing interagency
collaboration practices when it comes to data sharing and AI?
Dr. Tourassi. Again, speaking as a scientist, there are
expertise distributed across several different agencies, and
all these agencies need to come together to discuss how we need
to move forward. I can speak for the national laboratories that
they are an outstanding place as federally funded research and
development entities to serve as stewards of data assets and of
algorithms and to facilitate the benchmarking of datasets and
algorithms through the lifecycle of the algorithms, serving as
the neutral entities, and while using of course metrics that
are appropriate for the particular application domain and
driven by the appropriate other Federal agencies.
Mr. Baird. So one last question then that deals with your
prepared testimony. You described the problems that stem from
siloed data in health care. So that relates to what you just
mentioned, and you also mentioned the importance of integrating
nontraditional datasets, including social and economic data.
Briefly, I'm running close on time, so do you got any thoughts
on that----
Dr. Tourassi. You asked two different questions. As I
mentioned in my testimony, data is the currency not only for
AI, not only in the biomedical space but across all spaces. And
in the biomedical space we need to be very respectful about the
patient's privacy. And that has created silos in terms of where
the data reside and how we share the data. That in some ways
delays scientific innovation.
Mr. Baird. Thank you. And I wish I had time to ask the
other witnesses questions, but I'm out of time. I yield back,
Madam Chair.
Chairwoman Johnson. Thank you very much. Mr. Lipinski.
Mr. Lipinski. Thank you, Madam Chair. Thank you very much
for holding this hearing. I think this is something that we
should be spending a whole lot more time on. The impact that AI
is having and will have in the future is something we need to
examine very closely.
I really want to see AI develop. I understand all the great
benefits that can come from it, but there are ethical questions
that--tremendous number of things that we have not even had to
deal with before.
I have introduced the Growing Artificial Intelligence
Through Research, or GrAITR Act here in the House because I'm
concerned about the current state of AI R&D here in the U.S.
There's a Senate companion, which was introduced by my
colleagues Senators Heinrich, Portman, and Schatz. Now, I want
to make sure that we do the technical research but also have to
do the research and see what we may need to do here in Congress
to let--AI devices are developed consistent with our American
values.
I have focused a lot on this Committee because I'm a
political scientist. I focus a lot on the importance of social
science, and I think it's critically important that social
science is not left behind when it comes to being funded
because social science has applications to so much technology
and certainly in AI.
So I want to ask, when it comes to social science
research--and I'll start with Ms. Whittaker--what gaps do you
see in terms of the social science research that has been done
on AI, and what do you think can and should be done and what
should we be doing here in Washington about this?
Ms. Whittaker. Thank you. I love this question because I
firmly agree that we need a much more broad disciplinary
approach to studying AI. To date, most of the research done
concerning AI is technical research. Social science or other
disciplinary perspectives might be tacked on at the end, but
ultimately the study of AI has not traditionally been done
through a multi- or interdisciplinary lens.
And it's really important that we do this because the
technical component of AI is actually a fairly narrow piece.
When you begin to deploy AI in contexts like criminal justice
or hiring or education, you are integrating technology in
domains with their own histories, legal regimes, and
disciplinary expertise. So the fields with domain expertise
need to be incorporated at the center of the study of AI, to
help us understand the contexts and histories within which AI
systems are being applied.
At every step, from earliest development to deployment in a
given social context, we need to incorporate a much broader
range of perspectives, including the perspectives of the
communities whose lives and opportunities will be shaped by AI
decision making.
Mr. Lipinski. Mr. Clark?
Mr. Clark. OpenAI, we recently hired our first social
scientist, so that's one. We need obviously many more. And we
wrote an essay called, ``Why AI Safety Needs Social
Scientists.'' And the observation there is that, along with
everything Ms. Whittaker said, we should embed social
scientists with technical teams on projects because a lot of AI
projects are going to become about values, and technologists
are not great at understanding human values but social
scientists are and have tools to use and understand them. So my
specific pitch is to have federally funded Centers of
Excellence where you bring social scientists together with
technologists to work on applied things.
Mr. Lipinski. Thank you. Anyone else?
Mx. Buolamwini. So I would say in my own experience reading
from the social sciences actually enabled me to bring new
innovations to computer vision. So in particular my research
talks about intersectionality, which was introduced by Kimberle
Crenshaw, a legal scholar who is looking at antidiscrimination
law, and showed that if you only did single-access evaluation,
let's say you looked at discrimination by race or
discrimination by gender, people who were at the intersection
were being missed.
And I found that this was the same case for the evaluation
of the effectiveness of computer vision AI systems. So, for
example, when I did the test of Amazon, when you look at just
men or women, if you have a binary, if you look at darker skin
or lighter skin, you'll see some discrepancies. But when you do
an intersectional analysis, that's where we saw 0 percent error
rates for white men versus 31 percent error rates for women of
color. And it was that insight from the social sciences to
start thinking about looking at intersectionality. And so I
would posit that we not only look at social sciences being
something that is a help but as something that is integral.
Dr. Tourassi. As a STEM scientist, I do not speak to the
gaps in social sciences, but I know from my own work that for
AI technology to be truly impactful, the STEM scientists need
to be deeply embedded in the application space to work very
closely with the users so that we make sure that we answer the
right questions, not the questions that we want to answer as
engineers.
And in the biomedical space, we need to be thinking not
only about social sciences. We need to be thinking about
patient advocacy groups as well.
Chairwoman Johnson. Thank you very much. Dr. Babin.
Mr. Babin. Thank you, Madam Chair. Thank you, witnesses,
for being here today.
Mr. Clark and Dr. Tourassi, I have the privilege of
representing southeast Texas, which includes the Johnson Space
Center. And as the Ranking Member of the Subcommittee on Space
and Aeronautics, I've witnessed the diverse ways that NASA has
been able to use and develop AI, optimizing research and
exploration, and making our systems and technology much more
efficient.
Many of the new research missions at NASA have been
enhanced by AI in ways that were not previously even possible.
As a matter of fact, AI is a key piece to NASA's next rover
mission to Mars, and we could see the first mining of asteroids
in the Kuiper belt with the help of AI.
I say all of this to feature the ways that AI is used in
the area of data collection and space exploration but to
highlight private-public partnerships that have led to several
successful uses of AI in this field. Where do you see other
private-public partnership opportunities with Federal agencies
increasing the efficiency and the security using AI? Dr.
Tourassi, if you'll answer first, and then Mr. Clark.
Dr. Tourassi. So absolutely. The DOE national labs, as
federally funded research and development entities, we work
very closely with industry in terms of licensing and deploying
technology in a responsible way. So this is something that is
already hardwired in how we do science and how we translate
science.
Mr. Babin. Thank you very much. Mr. Clark.
Mr. Clark. My specific suggestion is joint work on
robustness, predictability, and broadly, safety, which
basically decodes to I have a big image classifier. A person
from industry and a person from government both want to know if
that's going to be safe and it will serve people effectively,
and we should pursue joint projects in this area.
Mr. Babin. Excellent. Thank you very much. And again, same
two, what would it mean for the United States if another
country were to gain dominance in AI, and how do we maintain
global leadership in this very important study and issue? Yes,
ma'am.
Dr. Tourassi. Absolutely it is imperative for our national
security and economic competitiveness that we maintain--we are
at the leading edge of the technology and we make responsible
R&D investments. In an area that I believe that we can lead the
world is that we can actually lead not only with the
technological advances but with what we talked about, socially
responsible AI. We can lead that dialog, that conversation for
the whole world.
Mr. Babin. Excellent.
Dr. Tourassi. And that differentiates us from other
entities investing in this space.
Mr. Babin. Yes, thank you. Thank you very much. Mr. Clark.
Mr. Clark. So I agree, but just to sort of reiterate this,
AI lets us encode values into systems that are then scaled
against sometimes entire populations, and so along with us
needing to work here in the United States on what appropriate
values are for these systems, which is its own piece of work,
as we've talked about, if we fail here, then the values that
our society lives under are partially determined by whichever
society wins in AI. And so the values that that society in
codes become the values that we experience. So I think the
stakes here are societal in nature, and we should not think of
this as about a technological challenge but how we as a society
want to become better. And the success here will be the ability
to articulate values that the rest of the world thinks are the
right ones to be embedded, so it's a big challenge.
Mr. Babin. It is a big challenge. If we do not maintain our
primacy in this, then other countries who might be a very
repressive with less, you know, lofty values that I assume
that's what you're talking about, could put these into effect
in a very detrimental way. So thank you very much. I appreciate
it, and I yield back, Madam Chair.
Chairwoman Johnson. Thank you very much. Ms. Bonamici.
Ms. Bonamici. Thank you to the Chair and the Ranking
Member, but really thank you to our panelists here.
I first want to note that the panel we have today is not
representative of people who work in the tech field, and I
think that that is something we need to be aware of because I
think it's still probably about 20 percent women, so I just
want to point that out.
This is an important conversation, and I'm glad we're
having it now. I think you've sent the message that it's not
too late, but we really need to raise awareness and figure out
if there's policies, if we're talking about the societal part.
We have here in this country some of the best scientists,
researchers, programmers, engineers, and we've seen some pretty
tremendous progress.
But over the years we've talked and spoken in this
Committee--and I represent a district in Oregon where we've had
lots of conversations about the challenges of integrating AI
into our society, what's happening with the workforce in that
area, but we really do need to understand better the
socioeconomic effects and especially the biases that it can
create. And I appreciate that you have brought those to our
attention, I mean, particularly for people of color.
And as my colleagues on this Committee know, I serve as the
Founder and Co-Chair of the congressional STEAM Caucus to
advocate for the integration of arts and design into STEM
fields. In The Innovators, author Walter Isaacson talked about
how the intersection of arts and science is where the digital
age creativity is going to occur.
STEAM education recognizes the benefits of both the arts
and sciences, and it can also create more inclusive classrooms,
especially in the K-12 system. And I wanted to ask Mx.
Buolamwini--I hope I said your name----
Mx. Buolamwini. Buolamwini.
Ms. Bonamici. I appreciate that in your testimony you
mentioned the creative science initiatives that are
incorporating the arts in outreach to more diverse audiences
that may never otherwise encounter information about the
challenges of AI. And I wonder if you could talk a little bit
about how we in Congress can support partnerships between
industry, academia, stakeholders to better increase awareness
about the biases that exist because until we have more
diversity--you know, it's all about what goes in, that sort of
algorithmic accountability I think if you will. And if we don't
have diversity going into the process, it's going to affect
what's coming out, so----
Mx. Buolamwini. Absolutely. So in addition to being a
computer scientist, I'm also a poet. And one of the ways I've
been getting the word out is through spoken word poetry. So I
just opened an art exhibition in the U.K. in the Barbican
that's a part of a 5-year traveling art show which is meant to
connect with people who might otherwise not encounter some of
the issues that are going on with AI.
Something I would love for Congress to do is to institute a
public-wide education campaign. Something I've been thinking
about is a project called Game of Tones, product testing for
inclusion. So what you could do----
Ms. Bonamici. Clever name already.
Mx. Buolamwini. So what you could do is use existing
consumer products so maybe it's voice recognition, tone of
voice, maybe it's what we're doing with analyzing social media
feeds, tone of text, maybe it's something that's to do with
computer vision, and use that as a way of showing how the
technologies people encounter every day can encode certain
sorts of problems, and most importantly, what can be done about
it. So it's not just we have these issues, but here are steps
forward, here are resources----
Ms. Bonamici. That's great.
Mx. Buolamwini [continuing]. You can reach out----
Ms. Bonamici. I serve on the Education Committee as well. I
really appreciate that.
Ms. Whittaker, your testimony talks about when these
systems fail, they fail in ways that harm those who are already
marginalized. And you mentioned that we have to encounter an AI
system that was biased against white men as a standalone
identity. So increasing diversity of course in the workforce is
an important first step, but what checks can we put in place to
make sure that historically marginalized communities are part
of the decisionmaking process that is leading up to the
deployment of AI?
Ms. Whittaker. Absolutely. Well, as we--as I discussed in
my written testimony and as AI Now's Rashida Richardson has
shown in her research, one thing we need to do is look at the
how the data we use to inform AI systems is created, because of
course all data is a reflection of the world as it is now, and
as it was in the past.
Ms. Bonamici. Right. Right.
Ms. Whittaker [continuing]. And the world of the past has a
sadly discriminatory history. So that data runs the risk of
imprinting biased histories of the past into the present and
the future, and scaling these discriminatory logics across our
core social institutions.
Ms. Bonamici. What efforts are being done at this point in
time to do that?
Ms. Whittaker. There are some efforts. A paper called
Datasheets for Datasets created a framework to provide AI
researchers and practitioners with information about the data
they were using to create AI systems, including information
about the collection and creation processes that shaped a given
dataset.
In a law review article titled ``Dirty Data, Bad
Predictions: How Civil Rights Violations Impact Police Data,
Predictive Policing Systems, and Justice,'' AI Now's Director
of Policy Research, Rashida Richardson, found that in at least
9 jurisdictions, police departments that were under government
oversight or investigation for racially biased or corrupt
policing practices were also deploying predictive policing
technology.
Ms. Bonamici. That's very concerning.
Ms. Whittaker [continuing]. What this means is that corrupt
and racist policing practices are creating the data that is
training these predictive systems. With no checks, and no
national standards on how that data is collected, validated,
and applied.
Ms. Bonamici. Thank you. And I see I've--my time is
expired. I yield back. Thank you, Madam Chair.
Chairwoman Johnson. Thank you very much. Mr. Marshall.
Mr. Marshall. Thank you, Madam Chair.
My first question for Dr. Tourassi, in your prepared
testimony you highlighted that the DOE's partnership with the
Cancer Institute Surveillance, Epidemiology, and End Results
program, can you explain the data collection process for this
program and how the data is kept secure? In what ways have you
noted the DOE accounts for artificial intelligence ethics,
bias, or reliability at this program? And you also mentioned
things like cancer biomarkers that AI are currently unable to
predict to produce information on this.
Dr. Tourassi. The particular partnership with the National
Cancer Surveillance program is organized as follows. Cancer is
a reportable disease in the U.S. and in other developed
countries. Therefore, every single cancer case that is detected
in the U.S. is recorded in the local registry. When the
partnership was established, the partnership included voluntary
participation of cancer registries that wanted to contribute
their data to advance R&D.
The data resides in the secure data enclave at the Oak
Ridge National Lab where we have the highest regulations and
accreditations for holding the data. Access to the data is
given responsibly to researchers from the DOE complex that have
the proper training to access the data, and that's--that is our
test bed for developing AI technology.
The first targets of the science was how we can develop
tools that help cancer registries become far more efficient in
what they do. It's not about replacing the individual. It's
actually helping them do something better and faster. So the
first set of tools that are deployed are exactly that, to
extract information from pathology reports that the cancer
registrars have to report on an annual basis to NCI, and we
free time for them to devote to other tasks that are far more
challenging for artificial intelligence and--such as the
biomarker extraction that you talked about.
Mr. Marshall. OK. Thank you so much. I'll address my next
question to Mr. Clark but then probably open it up to the rest
of the panel after that. How do you incentivize developers to
build appropriate safety and security into products when the
benefits may not be immediately evident to users?
Mr. Clark. I think technologists always love competing with
each other, and so I'm pretty bullish on the idea of creating
benchmarks and challenges which can encourage people to enter
systems into this. You can imagine competitions for who's got
the least biased system, which actually is something you can
imagine commercial companies wanting to participate in. You do
need to change the norms of the development community so that
individual developers see this as important, and that probably
requires earlier education and adding an ethics component to
developer education as well.
Mr. Marshall. OK. Ms. Whittaker, would you like to respond
as well?
Ms. Whittaker. Absolutely. I would add to what Mr. Clark's
points that it's also important to ensure the companies who
build and profit from these systems are held liable for any
harms. Companies are developing systems that are having a
profound impact on the lives and livelihoods of many members of
the public. These companies should be responsible for those
impacts, and those with the most power inside these companies
should be held most responsible. This is an important point,
since most AI developers are not working alone, but are
employed within one of these organizations, and the incentives
and drivers governing their work are shaped by the incentives
of large tech corporations.
Mr. Marshall. OK, thanks. Yes, Mx. Buolamwini, sorry I
missed the introductions there.
Mx. Buolamwini. Buolamwini. You're fine. And so something
else we might consider is something akin to public interest law
clinics but are meant for public interest technology so that
it's part of your computer science or AI education that you're
working with a clinic that's also connected to communities that
are actually harmed by some of these processes. So it's part of
how you come to learn.
Mr. Marshall. OK. Thanks. And, Dr. Tourassi, you get to bat
cleanup. Anything you want to add?
Dr. Tourassi. I don't really have anything to add to this
question. I think the other panelists captured it very well.
Mr. Marshall. Yes, thank you so much, and I yield back.
Chairwoman Johnson. Thank you very much. Ms. Sherrill.
Ms. Sherrill. Thank you. And thank you to all the panelists
for coming today.
This hearing is on the societal and ethical implications of
AI, and I'm really interested in the societal dimension when it
comes to the impact AI is having on the workforce and how it's
increasingly going to shape the future of work. So my first
question to the panel is what will the shift in AI mean for
jobs across the country? Will the shift to an economy
increasingly transformed by AI be evenly distributed across
regions, across ethnic groups, across men and women? Will it be
evenly distributed throughout our job sectors? And how do you
see the percentages of how AI is impacting the workforce
changing over the years? Which portion of our workforce will be
impacted directly by AI and how will that look for society?
Ms. Whittaker. Thank you. Well, I think we're already
seeing AI impact the workforce and impact what it means to have
a job. We're seeing AI integrated into hiring and recruiting. A
company called HireVue now offers video interview services that
claim to be able to tell whether somebody is a good candidate
based on the way they move their face, their micro-expressions,
their tone of voice. Now, how this works across different
populations and different skin tones and different genders is
unclear because this technology is proprietary, and thus not
subject to auditing and public scrutiny.
We are seeing AI integrated into management and worker
control. A company called Cogito offers a service to call
centers that will monitor the tone of voice and the affect of
people on the phone and give them instructions to be more
empathetic, or to close the call. It also sends their managers
a ranking of how they're doing, and performance assessments can
then be based on whatever the machine determines this person is
doing well or doing poorly.
We're seeing similar mechanisms in Amazon warehouses where
workers' productivity rates are being set by algorithms that
are calibrated to continually extract more and more labor.
We've actually seen workers in Michigan walk out of warehouses
protesting what they consider inhumane algorithmic management.
Overall, we are already seeing the nature of work reshaped
by AI and algorithmic systems, which rely on worker tracking
and surveillance and leave no room for workers to contest or
even consent to the use of such systems. Ultimately, this
increases the power of employers, and significantly weakens the
power of workers.
Ms. Sherrill. And what about--and I'll get--we can go back
to you, too, and you can go back to the question if you want,
Mx. Buolamwini, but what--to what extent is it going to
transfer the ability of people to get jobs and get into the
workforce?
Mx. Buolamwini. So one thing I wanted to touch upon is how
AI is being used to terminate jobs and something I call the
exclusion overhead where people who are not designed for the
system have to extend more energy to actually be a part of the
system. One example comes from several reports of transgendered
drivers being kicked off of Uber accounts because when they
used a fraud detection system, which uses facial recognition to
see if you are who you say you are, given that they present
differently, there were more checks required. So one driver
reported that over an 18-month period she actually had to
undergo 100 different checks, and then eventually her account
was deactivated.
On May 20, another Uber driver actually sued Uber for more
than $200,000 after having his account deactivated because he
had to lighten his photos so that his face could be seen by
these systems, and then there was no kind of recourse, no due
process and that he couldn't even reach out to say the reason I
lightened my photo, right, was because the system wasn't
detecting me.
Ms. Sherrill. It was failing?
Mx. Buolamwini. Yes.
Ms. Sherrill. And so also in my district I--and this is to
the panel again. I've seen our community colleges and
polytechnical schools engaging in conversations with businesses
about how they can best train workers to meet the new
challenges of the AI workforce and provide them with the
skills. Structurally, how does secondary education need to
adjust to be able to adapt to the changing needs and the
changing challenges that you're outlining? How can we better
prepare students to enter into this workforce?
Mr. Clark. I'll just do a very quick point. We do not have
the data to say how AI will affect the economy. We have strong
intuitions from everyone who works in AI that it will affect
the economy dramatically. And so I'd say before we think about
what we need to teach children, we need a real study of how
it's impacting things. None of us are able to give you a number
on employment----
Ms. Sherrill. And just because I have 6 seconds, what would
you suggest to us to focus on in that study?
Mr. Clark. I think it would be useful to look at the
tractability for in-development technologies to be applied at
large scale throughout the economy and to look at the economic
impacts of existing things like how we've automated visual
analysis and what economic impacts that has had because it's
been dramatic but we don't have the data from which to talk
about it.
Ms. Sherrill. Thank you. I yield back. Thank you, Madam
Chair.
Chairwoman Johnson. Thank you very much. Mr. Gonzalez.
Mr. Gonzalez. Thank you, Madam Chair, and thank you to our
panel for being here today on this very important topic.
Mr. Clark, I want to start my line of questioning with you.
It's my belief that the United States needs to lead on machine
learning and AI if for no other reason for the sake of
standards development, especially when you think about the
economic race between ourselves and China. One, I guess, first
question, do you share that concern; and then two, if yes, what
concerns would you have in a world where China is the one that
is sort of leading the AI evolution if you will and dictating
standards globally?
Mr. Clark. Yes, I agree. And to answer your second
question, I think if you don't define the standard, then you
have less ability to figure out how the standard is going to
change your economy and how you can change industry around it,
so it just puts you behind the curve. It means that your
economic advantage is going to be less, you're going to be less
well-oriented in the space, and if you don't invest in the
people to go and make those standards, then you're going to
have lots of highly qualified reasonable people from China
making those. And they'll develop skills, and then we won't get
to make them.
Mr. Gonzalez. Yes, thank you. And then, Dr. Tourassi,
another question that I have is around data ownership and data
privacy. You know, we talk about the promise of AI a lot, and
it is certainly there. I don't know that we talk enough about
how to empower individuals with control over their data who are
ultimately the ones providing the value by--without even
choosing to provide all this data. So in your opinion how
should we at the Committee level and as a Congress think about
balancing that tradeoff between data privacy and ownership for
the individual and the innovation that we know is coming?
Dr. Tourassi. This is actually an excellent question and
fundamental in the healthcare space because, in the end, all
the AI algorithmic advances that are happening wouldn't be
possible if the patients did not contribute their data and if
the healthcare providers did not provide the services that
collect the data. So in the end who owns the product?
This is a conversation that requires a societal--as a
society to have these pointed conversations about these issues
and to bring all the different stakeholders into place. Privacy
and ownership mean different things to different people. One
size will not fit all. We need to have--to build a framework in
place so that we can address these questions per application
domain, per situation that arises.
Mr. Gonzalez. Thank you. And sort of--this one's maybe for
everybody, sort of a take on that. Deep fakes is something that
we've been hearing a little bit more of lately, and I think the
risk here is profound where we get into a world where you
literally cannot tell the difference between me calling you on
the phone physically or a machine producing my voice. So as we
think about that, I guess my question would be, how can the NSF
or other Federal agencies ensure that we have the tools
available to detect these deep fakes as they come into our
society? We'll start with Ms. Whittaker.
Ms. Whittaker. Well, I think this is an area where we need
much more research funding and much more development. I would
also expand the--this answer to include looking at the
environments in which such sensational content might thrive.
And so you're looking at engagement-driven algorithmic systems
like Facebook, Twitter, and YouTube. And I think addressing the
way in which those algorithms surface sensational content is
something that needs to go hand-in-hand with detection efforts
because, fundamentally, there is an ecology that rests below
the surface that is promoting the use of these kind of content.
Mr. Gonzalez. I completely agree. Thank you.
Mr. Clark. I agree with these points, and I'd just make one
point in addition----
Mr. Gonzalez. Yes.
Mr. Clark [continuing]. Which is that we need to know where
these technologies are going. We could have had a conversation
about deep fakes 2 years ago if you look at the research
literature----
Mr. Gonzalez. Yes.
Mr. Clark [continuing]. And government should invest to
look at the literature today because there will be other
challenges similar to deep fakes in our future.
Mx. Buolamwini. We also need to invest in AI literacy where
you know that there will be people deploying AI in ways that
are meant to be intentionally harmful. So I think making sure
people have an awareness that deep fakes can exist and other
ways of deception that can arise from AI systems exist as well.
Mr. Gonzalez. Thank you.
Dr. Tourassi. So adversarial use of AI technology is a
reality.
Mr. Gonzalez. Yes.
Dr. Tourassi. It's here. Therefore, the investments in R&D
and having an entity that will serve as the neutral entity to
steward--to be the steward of the technology and the datasets
is a very important piece that we need to consider very
carefully and make calculated investments. This is not a one-
time solution. Something is clean, ready to go. The
vulnerabilities will always exist, so we need to have the
processes and the entities in place to mitigate the risks.
And I go back to my philosophy. I believe in what Marie
Curie said, ``There is nothing to be feared, only to be
understood.'' So let's make the R&D investments to understand.
Make the most of the potential and mitigate the risks.
Mr. Gonzalez. Thank you. I yield back.
Chairwoman Johnson. Thank you very much. Mr. McNerney.
Mr. McNerney. Well, thank you. I thank the Chairwoman, and
I thank the panelists. The testimony is excellent. I think you
all have some recommendations that are good and are going to be
helpful in guiding us to move forward, but I want to look at
some of those recommendations.
One of your recommendations, Ms. Whittaker, is to require
tech companies to waive their secrecy. Now, that sounds great,
but in practice it's going to be pretty difficult, especially
in light of our competition on the international scene with
China and other countries. How do you envision that happening?
How do you envision tech companies opening up their trade
secrets without losing the--you know, the competition.
Ms. Whittaker. Yes, absolutely. And, as I expand on in my
written testimony, this isn't--the vision of this
recommendation is not simply that tech companies throw open the
door and everything is open to everyone. This is specifically
looking at claims of trade secrecy that are preventing
accountability. Ultimately, we need public oversight, and
overly broad claims to trade secrecy are making that extremely
difficult. A nudge from regulators would help here.
We need provisions that waive trade secrecy for independent
auditors, for researchers examining issues of bias and fairness
and inaccuracy, and for those examining the contexts within
which AI systems are being licensed and applied. That last
point is important. A lot of the AI that's being deployed in
core social domains is created by large tech companies, who
license this AI to third parties. Call it an "AI as a service"
business model. These third parties apply tech company AI in a
variety of contexts. But the public rarely knows where and how
it's being used, because the contracts between the tech
companies and the third parties are usually secret.
Even the fact that there is a contract between, say, Amazon
and another entity to license, say, facial recognition is not
something that the public who would be profiled by such systems
would know. And that makes tracing issues of bias, issues of
basic freedoms, issues of misuse extremely hard.
Mr. McNerney. Thank you for that answer. Mr. Clark, I love
the way you said that AI encodes the value system of its
coders. You cited three recommendations. Do you think those
three recommendations you cited will ensure a broader set of
values would be incorporated in AI systems?
Mr. Clark. I described them as necessary but not
sufficient. I think that they need to be done along with a
larger series of things to incur values. Values is a personal
question. It's about how we as a society evaluate what fairness
means in a commercial marketplace. And I think that AI is going
to highlight all of the ways in which our current systems for
sort of determining that need additional work. So I don't have
additional suggestions beyond those I make, but is suspect
they're out there.
Mr. McNerney. And the idea to have NIST create standards, I
mean, that sounds a good idea.
Mr. Clark. Yes, my general observation is we have a large
number of great research efforts being done on bias and issues
like it, and if we have a part of government convene those
efforts and create testing suites, we can create the source of
loose standards that other people can start to test in, and it
generates more data for the research community to make
recommendations from.
Mr. McNerney. Thank you. Mx. Buolamwini, you recommended a
5 percent AI accountability tax. How did you arrive at that
figure, and how do you see that being implemented?
Mx. Buolamwini. So this one was a 0.5 percent tax, and
you----
Mr. McNerney. Point 5 percent, thank you.
Mx. Buolamwini [continuing]. And you have the Algorithmic
Accountability Act of 2019 that was sponsored by Representative
Yvette Clark. And I think it could be something that is added
to that particular piece of legislation. And so the requirement
that they specifically have is this would be for companies that
are making over $50 million in revenue or average gross, and
then also it would either apply to companies that have or
possess over one million consumer devices or reach more than
one million consumers. So I could see it being integrated into
a larger framework that's already about algorithmic
accountability.
Mr. McNerney. Thank you. Ms. Whittaker and Mx. Buolamwini,
you both advocated--in fact, all of you did--for a more diverse
workforce. I've written legislation to do that. It really
doesn't go anywhere around here. What's a realistic way to get
that done? How do we diversify the workforce here?
Ms. Whittaker. I would hope that lawmakers continue to push
legislation that would address diversity in tech, because put
frankly, we have a diversity crisis on our hands. It has not
gotten better; it has gotten worse in spite of years and years
of diversity rhetoric and P.R. We're looking at an industry
where----
Mr. McNerney. So you think government is the right tool to
make that happen?
Ms. Whittaker. I think we need to use as many tools as we
have. I think we need to mandate pay equity and transparency.
We need to mandate much more thorough protections for people
who are the victims of sexual harassment in the workplace. This
is a problem that tech has. At Google, for example, more than
half of the workforce is made up of contract workers. And this
is true across all job types, not just janitors and service
workers. You have engineers, designers, project managers,
working alongside their full-time colleagues, without the
privileges of full employment, and thus without the safety to
push back against inequity.
I would add that we also need to look at the practice of
hiring increasing numbers of contract workers. These workers
are extremely vulnerable to harassment and discrimination. They
don't have the protection of full-time employees. And you have
seen at Google at this point more than half the workforce is
made up of contract workers across all job types, so this isn't
just janitorial staff or service workers. This is engineers,
designers, team leads that don't have the privileges of full
employment and thus don't have the safety to push back against
inequity.
Mr. McNerney. I've run out of time, so I can't pursue that.
I yield back.
Chairwoman Johnson. Thank you very much. Miss Gonzalez-
Colon.
Miss Gonzalez-Colon. Thank you, Madam Chair. And yes, I
have two questions. Sorry, I was running from another markup.
Dr. Tourassi, the University of Puerto Rico, Mayaguez Campus,
which is in my district, is an artificial intelligence
education and research institute. The facility exposes young
students to the field of artificial intelligence. Their core
mission is to advance knowledge and provide education in
artificial intelligence in theory, methods, system, and
applications to human society and to economic prosperity.
My question will be, in your view, how can we engage with
institutes of higher education to promote similar initiatives
or efforts, keeping in mind generating interest in artificial
intelligence in young students from all areas and how can we be
secure that what is produced later on is responsible, ethical,
and financially profitable?
Dr. Tourassi. So, as you mentioned, the earlier we start
recruiting workforce, our trainees that reflect the actual
workforce with education and the diversity that is needed, that
is extremely important. When the AI developers reflect the
actual user community, then we know that we have arrived. That
cannot be achieved only with academic institutions. This is a
societal responsibility for all of us.
I can tell how the national laboratories are working in
this space. We are enhancing the academic places and
opportunities by offering internship opportunities to students
who haven't otherwise--they do not come from research
institutions, and this is the first time for them that they can
work in a thriving research place. So we need to be thinking
more outside the box and how we can all work synergistically
and continuously on this.
Miss Gonzalez-Colon. Thank you. I want to share with you as
well that my office recently had a meeting with a
representative of this panel organization, and they were
commenting of the challenges they have on approaching American
manufacturers, specifically car manufacturers on accessible
autonomous vehicles. Several constituents with disabilities
rely on them or on similar equipment for maintaining some
degree of independence and rehabilitation. My question would
be, in your view how can we engage that private-sector--you
were just talking a few seconds ago--and the manufacturers
that--so we not only ensure that artificial intelligence
products are ethical and inclusive but provide opportunities
for all sectors of the community, in other words, make this
working for everyone? How can we arrange that?
Dr. Tourassi. If I understood your question, you're asking
how we can build more effective bridges?
Miss Gonzalez-Colon. In your view, yes, it's kind of the
same thing.
Dr. Tourassi. And again, I can speak to how we are building
these bridges as national laboratories working with both
academic and research institutions, as well as with private
industry creating very thriving hubs for researchers to engage
in societally impactful science and develop solutions, end-to-
end solutions from R&D all the way to the translation of these
products. I see the federally funded R&D entities such as
national labs being one form of these bridges.
Miss Gonzalez-Colon. How can people with disabilities be
counted for when we talk about artificial intelligence?
Dr. Tourassi. Well, as I said, one size will not fit all.
It will come down to the particular application domain, so it
is our responsibility as scientists to be mindful of that. And
while working, deeply embedded in the application space with
the other sciences that will educate us on where the gaps are,
that's how we can save ourselves from all the blind spots.
Miss Gonzalez-Colon. You said in your testimony--you
highlighted the importance of an inclusive and diverse
artificial intelligence workforce. For you, what is the
greatest challenge in the United States of developing this kind
of workforce?
Dr. Tourassi. As a female STEM scientist and often the
token woman for the past three decades in the field, the
biggest challenge we have is not actually recruiting a diverse
set of trainees but also sustaining them in the workforce. And
I passionately believe that we need to change our notion of
what is leadership. There are different models of leadership
and the more we become comfortable with different styles of
leadership. In my own group, in my own team, I make sure that I
have a very diverse group of researchers, including people with
disabilities, doing phenomenal AI research work. So it comes
down to not only developing policies but what is our also
individual responsibility as citizens.
Miss Gonzalez-Colon. Thank you. And I yield back.
Chairwoman Johnson. Thank you very much. Mr. Tonko.
Mr. Tonko. Thank you, Chairwoman Johnson, for holding the
hearing, and thank you to our witnesses for joining us.
Artificial intelligence is sparking revolutionary change
across industries and fields of study. Its benefits will drive
progress in health care, climate change, energy, and more. AI
can help us diagnose diseases early by tracking patterns of
personal medical history. It can help identify developing
weather systems, providing early warning to help communities
escape harm.
Across my home State of New York, companies, labs, and
universities are conducting innovative research and education
in AI, including the AI Now Institute at New York University
represented here with us today by Co-Founder Meredith
Whittaker. Students at Rensselaer Polytechnic Institute in Troy
studying machine logic at the Rensselaer AI and Reasoning Lab--
work that could transform our understanding of human-machine
communication.
IBM and SUNY Polytechnic Institute have formed a
groundbreaking partnership to develop an AI hardware lab in
Albany focused on developing computer chips and other AI
hardware. That partnership is part of a broader $2 billion
commitment by IBM in my home State. This work is more than
technical robotics. University of Albany researchers are
working on ways to detect AI generated deep fake video
alterations to prevent the spread of fake news, an issue that
has already impacted some of our colleagues in Congress. These
researchers are using metrics such as human blinking rates to
weed out deep fake videos from authentic ones.
AI presented great benefits, but it is a double-edged
sword. In some studies, AI was able to identify individuals at
risk for mental health conditions just by scanning their social
media accounts. This can help medical professionals identify
and treat those most at risk, but it also raises privacy issues
for individuals.
We have also seen evidence of data and technical bias that
underrepresents or misrepresents people of color in everything
from facial recognition to Instagram filters. As a Committee, I
am confident that we will continue to explore both the benefits
and risks associated with AI, and I look forward to learning
more from our witnesses today.
And my question for all panelists is this: What is an
example that illustrates the potential of AI? And what is an
example that illustrates the risks? Anyone? Ms. Whittaker.
Ms. Whittaker. Yes, I will use the same example for both
because I think this gives a sense of the double-edged sword of
this technology. Google's DeepMind research lab applied AI
technology to reduce the energy consumption of Google's data
centers. And by doing this, they claim to have reduced Google's
data center energy bill by 40%. They did this by training AI on
data collected from these data centers, and using it to
optimizing things like when a cooling fan was turned on, and
otherwise much more precisely calibrate energy use to ensure
maximum efficiency. So here we have an example of AI being used
in ways that can reduce energy consumption, and potentially
address climate issues.
But we've also seen recent research that exposes the
massive energy cost of creating AI systems, specifically the
vast computational infrastructure needed to train AI models. A
recent study showed that the amount of carbon produced in the
process of training one natural language processing AI model
was the same as the amount produced by five cars over their
lifetimes. So even if AI, when it's applied, can help with
energy consumption, we're not currently accounting for the vast
consumption required to produce and maintain AI technologies.
Mr. Tonko. Thank you. Anyone else?
Mr. Clark. Very, very quickly----
Mr. Tonko. Mr. Clark.
Mr. Clark. One of the big potentials of AI is in health
care and specifically sharing datasets across not just, you
know, States and local boundaries but eventually across
countries. I think we can create global-class diagnostic
systems to save people's lives.
Now, a risk is that all of these things need to be
evaluated empirically after we've created them for things like
bias, and I think that we lack the tools, funding, and
institutions to do that empirical evaluation of developed
systems safely.
Mr. Tonko. OK. Mx. Buolamwini?
Mx. Buolamwini. Yes, so I look at computer vision systems
where I see both cost for inclusion and cost for exclusion. So
when you're using a vision system to, say, detect a pedestrian,
you would likely want that to be as accurate as possible as to
not hit individuals, but that's also the same kind of
technology you could put on a drone with a gun to target an
individual as well. So making sure that we're balancing the
cost of inclusion and the cost of exclusion and putting in
context limitations where you say there are certain categorical
uses we are not considering.
Mr. Tonko. Thank you. And Dr. Tourassi, please?
Dr. Tourassi. Yes. I agree with Mr. Clark that in the
healthcare space the promise of AI is evident with clinical
decision support systems, for example, for reducing the risk of
medical error in the diagnostic interpretation of systems.
However, that same field that shows many great examples is full
of studies that overhype expectations of universal benefits
because these studies are limited to one medical center, to a
small population.
So we need to become, as I said, educated consumers of the
technology and the hype, the news that are out there. We need
to be asking these questions, how extensively this tool has
been used, across how many populations, how many States, how
many--when we dive into the details and we do that benchmarking
that Mr. Clark alluded to, then we know that the promise is
real. And there are studies that have done that with the rigor
required.
Mr. Tonko. Thank you so much. And with that, I yield back,
Madam Chairwoman.
Chairwoman Johnson. Thank you very much. Mr. Beyer.
Mr. Beyer. Thank you, Madam Chair. And thank you for
holding this hearing. I really want to thank our four panelists
for really responsible, credible testimony. I'm going to save
all of these printed texts and share them with many friends.
You know, the last 4 years on the Science Committee, AI has
come up again and again and again. And we've only had glancing
blows at the ethics or the societal implications. We've mostly
been talking the math and the machine learning and the promise.
Even yesterday, we had Secretary Rick Perry--I can't remember
which department he represents, but he was here yesterday--just
kidding--raving about artificial intelligence and machine
learning.
And thanks, too, for the concrete recommendations; we don't
often always get that in the Science Committee. But I counted.
There were 24 concrete recommendations that you guys offered,
everything from waiving trade secrecy to benchmarking machine
learning for its societally harmful failures to even an AI tax,
which my friends on Ways and Means will love.
But the one ethical societal failure that we haven't talked
about is sort of driven by everything you did. One of your
papers talked about the 300,000-time increase in machine-
learning power in the last 5 or 6 years compared to Moore's
law, which would have been 12 times in the same time. In
Virginia, we have something like 35,000 AI jobs we're looking
to fill right now. And one of the other papers talked about
awareness. And we have certainly had computer scientists here
in the last couple of years who talked about ambition
awareness.
So let me ask the Skynet question. What do you do about the
big picture when--well, as my daughter already says, Wall
Street is almost completely run right now by machine learning.
I mean, it's all algorithms. I visited the floor of the New
York Stock Exchange a couple weeks ago with the Ways and Means
Committee, and there were very few people there. The people all
disappeared. It's all done algorithmically.
So let's talk about the big picture. Any thoughts on the
big-picture societal implication of when AI is running all the
rest of our lives?
Mr. Clark. I think it's pretty clear that AI systems are
scaling and they're going to become more capable and at some
point we'll allow them to have larger amounts of autonomy. I
think the responsible thing to do is to build institutions
today that will be robust to really powerful AI systems. And
that's why I'm calling for large-scale measurement assessments
and benchmarking of existing systems deployed today. And that's
because if we do that work today, then as the systems change,
we'll have the institutions that we can draw on to assess the
growing opportunities and threats of these systems. I really
think it's as simple as being able to do weather forecasting
for this technical progress, and we lack that infrastructure
today.
Mr. Beyer. Mx. Buolamwini, I'm going to mispronounce your
name, but you're at MIT, you're right next to Steve Pinker at
Harvard. They're doing all this amazing work on the evolution
of consciousness and consciousness as an emergent property, one
you don't necessarily intend, but there it is. Shouldn't we
worry about emergent consciousness in AI, especially as we
build capacity?
Mx. Buolamwini. I mean, the worry about conscious AI I
think sometimes misses the real-world issues of dumb AI, AIs
that are not well-trained, right? So when I go back to an
example I did in my opening statement, I talk about a recent
study that came out showing pedestrian tracking technologies
had a higher miss rate for children, right, as compared to
adults. So here we were worried about the AIs becoming
sentient, and the ones that are leading to the fatalities are
the ones that weren't even well-trained.
Mr. Beyer. Well, I would be grateful--among the 24
thoughtful, excellent suggestions you made--and hopefully, we
will follow up on many of them or the ones that are
congressionally appropriate--is one more that doesn't deal with
the kids that get killed, which is totally good, you know, the
issues of ageism, sexism, racism that show up, those are all
very, very meaningful, but I think we also need to look long
term, which is what good leaders do--about the sentience issue
and how we protect, not necessarily make sure it doesn't happen
but how we protect. And thank you very much for being part of
this.
Madam Chair, I yield back.
Chairwoman Johnson. Thank you very much. Mr. Lamb.
Mr. Lamb. Thank you, Madam Chairwoman. A couple of you have
hit on some issues about AI as it relates to working people
both in the hiring process, you know, discriminating against
who they're going to hire and bias embedded in what they're
doing, as well as the concerns about AI just displacing
people's jobs. But I was wondering if any of you could go into
a little more detail on AI in the existing workplace and how it
might be used to control working people, to worsen their
working conditions. I can envision artificial intelligence
applications that could sort of interrupt nascent efforts to
organize a workplace or maybe in an organized workplace a union
that wants to bargain over the future of AI in that workplace
but they're not able to access the sort of data to understand
what it even is they're bargaining over.
So I don't know if you can give any examples from present-
day where these types of things are already happening or just
address what we can do to take on those problems as they evolve
because I think they're going to come. Thank you.
Ms. Whittaker. Thank you. Yes, I can provide a couple of
examples, and I'll start by saying that, as my Co-Founder at AI
Now Kate Crawford and the legal scholar Jason Schultz have
pointed out, there are basically no protections for worker
privacy. AI relies on data, and there are many companies and
services currently offering to surveil and collect data on
workers. And there are many companies that are now offering the
capacity to analyze that data and make determinations based on
that analysis. And a lot of the claims based on such analysis
have no grounding in science. Things like, ``is a worker typing
in a way that matches the data-profile of someone likely to
quit?'' Whether or not typing style can predict attrition has
not been tested or confirmed by any evidence, but nonetheless
services are being sold to employers that claim to be able to
make the connection. And that means that even though they're
pseudoscientific, these claims are powerful. Managers and
bosses are acting on such determinations, in ways that are
shaping people's lives and livelihoods. And workers have no way
to push back and contest such claims. We urgently need stronger
worker privacy protections, standards that allow workers to
contest the determinations made by such systems, and
enforceable standards of scientific validation.
I can provide a couple of examples of where we're seeing
worker pushback against this kind of AI. Again, I mentioned the
Amazon warehouse workers. We learned recently that Amazon uses
a management algorithm in their warehouses. This algorithm
tracks worker performance based on data from a sensor that
workers are required to wear on their wrist, looking at how
well workers are performing in relation to an algorithmically-
set performance rate. If a worker misses their rate, the
algorithm can issue automatic performance warnings. And if a
worker misses their rate too many times--say, they have to go
to the bathroom, or deal with a family emergency--the algorithm
can automatically terminate them. What becomes clear in
examining Amazon's management algorithm, is that these are
systems created by those in power, by employers, and designed
to extract as much labor as possible out of workers, without
giving them any possible recourse.
We have also seen Uber drivers striking, around the time of
the Uber IPO. In this case, they were protesting a similar
technologically-enabled power imbalance, which manifested in
Uber arbitrarily cutting their wages without any warning or
explanation. Again, we see such tech being used by employers to
increase power asymmetry between workers, and those at the top.
A couple of years ago we saw the massive Virginia teachers
strike. What wasn't widely reported was one of the reasons for
this strike: the insistence by the school district that
teachers wear health tracking devices as a condition of
receiving health insurance. These devices collect extremely
personal data, which is often processed and analyzed using AI.
You've also seen students protesting AI-enabled education,
from Brooklyn, to Kansas, and beyond. Many of these programs
were marketed as breakthroughs that would enable personalized
learning. What they actually did was force children to sit in
front of screens all day, with little social interaction or
personal attention from teachers.
In short, we've seen many, many examples of people pushing
back against the automation of management, and the unchecked
centralized power that AI systems are providing employers, at
the expense of workers.
Finally, we've also seen tech workers at these companies
organizing around many of these issues. I've been a part of a
number of these organizing efforts, which are questioning the
process by which such systems are created. Tech workers
recognize the dangers of these technologies, and many are
saying that they don't want to take part in building unethical
systems that will be used to surveil and control. Tech workers
know that we have almost no checks or oversight of these
technologies, and are extremely concerned that they will be
used for exploitation, extraction, and harm. There is mounting
evidence that they are right to be concerned.
Mr. Lamb. Thank you very much. I'm just going to ask one
more question. I'm almost out of time. Ms. Tourassi--or, Dr.
Tourassi, I'm sorry, I know that Oak Ridge has been a partner
with the Veterans Health Administration, MVP-CHAMPION I think
it's called, and if you could just talk a little bit about--is
that project an example of the way that the VA can be a leader
in AI as it relates to medicine, precision medicine? You know,
we've got this seven-million veteran patient population, and in
a number of IT areas we think of it as a leader that can help
advance the field. Are you seeing that or are there more things
we could be doing?
Dr. Tourassi. The particular program you described, it's
part of the Strategic Partnerships Program that brings the AI
and high-performance computing expertise that exist within the
DOE national lab system with the application domain and
effectively the data owners as well. So that partnership is
what's pushing the field forward in terms of developing
technologies that we can deploy in the environment of the VA
Administration to improve veterans' health care.
I wouldn't consider the Veterans Administration as
spearheading artificial intelligence, but, as I said in my
written testimony, talent alone is not enough. You need to have
the data, you have to--you need to have the compute resources,
and you need to have talent. The two entities coming together,
they create that perfect synergy to move the field forward.
Mr. Lamb. Well, thank you for that. And I do believe that
labs like yours and the efforts that we make in the VHA system
are a way that we can help push back against the bias and
discrimination in this field because the government really at
its best has tried to be a leader in building a diverse
workforce of all kinds and allowing workers at least in the
Veterans Administration to organize and be part of this whole
discussion, so hopefully we can keep moving that forward.
Madam Chair, I yield back. Thank you.
Chairwoman Johnson. Thank you very much. Mr. Sherman.
Mr. Sherman. Thank you. I've been in Congress for about 23
years, and in every Committee we focus on diversity, economic
disruption, wages, and privacy. And we've dealt with that here
today as well.
I want to focus on something else that is more than a
decade away, and that is that the most explosive power in the
universe is intelligence. Two hundred thousand years ago or so
our ancestors said hello to Neanderthal. It did not work out
well for Neanderthal. That was the last time a new level of
intelligence came to this planet, and it looks like we're going
to see something similar again, only we are the Neanderthal.
We have, in effect, two competing teams. We have the
computer engineers represented here before us developing new
levels of intelligence, and we have the genetic engineers quite
capable in the decades to come of inventing a mammal with a
brain--hundreds of pounds.
So the issue before us today is whether our successor
species will be carbon-based or silicon-based, whether the
planet will be inherited by those with artificial intelligence
or biologically engineered intelligence.
There are those who say that we don't have to fear any
computer because it doesn't have hands. It's in a box; it can't
affect our world. Let me assure you that there are many in our
species that would give hands to the devil in return for a good
stock tip.
The chief difference between the artificial intelligence
and the genetically engineered intelligence is survival
instinct. With DNA, it's programmed in. You try to kill a bug,
it seems to want to survive. It has a survival instinct. And
you can call it survival instinct; you could call it ambition.
You go to turn off your washing machine or even the biggest
computer that you've worked with, you go to unplug it, it
doesn't seem to care.
What amount of--what percentage of all the research being
done on artificial intelligence is being used to detect and
prevent self-awareness and ambition? Does anybody have an
answer to that? Otherwise, I'll ask you to answer for the
record. Yes, sir.
Mr. Clark. We have an AI safety team at OpenAI, and a lot
of that work is about--if I set an objective for a computer, it
will probably solve that objective, but it will sometimes do--
solve that objective in a way that is incredibly harmful to
people because, as other panelists have said, these algorithms
are kind of dumb.
Mr. Sherman. Right.
Mr. Clark. What you can do is you can try and have these
systems learn values from people.
Mr. Sherman. Learning values is nice. What are you doing to
prevent self-awareness and ambition?
Mr. Clark. The idea is that if we encode the values that
people have into these systems and so----
Mr. Sherman. I don't want to be replaced by a really nice
new form of intelligence. I'm looking for a tool that doesn't
seek to affect the world.
I want to move onto another issue, related though. I think
you're familiar with the Turing test, which in the 1950s was
proposed as the way we would know that computers had reached or
exceeded human intelligence, and that is could you have a
conversation with a computer and not know you're having a
conversation with a computer? In this room in 2003 top experts
of then predicted that the Turing test would be met by 2028.
Does anybody here have a different view? Is that as good an
estimate as any? They said it would be 25 years, and that was
back in 2003.
I'm not seeing anybody jump up with a different estimate,
so I guess we have that one. You're not quite jumping up, but
go ahead.
Ms. Whittaker. I don't have an estimate on that. I do
question the validity of the Turing test insofar as it relies
on us to define what a human is, which is of course a
philosophical question that we could debate for hours.
Mr. Sherman. Well, I don't know about philosophers, but the
law pretty well defines who's a human and who isn't and, of
course, if we invent new kinds of sentient beings, the law will
have to grow.
I just want to add Mr. Beyer brought this up and was kind
of dismissed by the idea that we shouldn't worry about a new
level of intelligence since we, as of yet, don't have a
computer that can drive a car without hitting a child. I think
it's important that if we're going to have computers drive cars
that they not hit children, but that's not a reason to dismiss
the fact that between biological engineering and computer
engineering, we are the Neanderthal creating our own Cro-
Magnon.
I yield back.
Chairwoman Johnson. Thank you very much. Ms. Horn.
Ms. Horn. Thank you, Madam Chairwoman. And thank you to the
panel for an important and interesting conversation today.
I think it's clear that each time we, as society or as
humans, experience a massive technological shift or
advancement, it brings with it both opportunities and ways to
make our life better or easier or move more smoothly and also
challenges and dangers that are unknown to us in the
development of that. And what I've heard from several of you
today goes to the heart of this conversation, the need to
balance the ethical, social, and legal implications with the
technological advancement and the need to incorporate that from
the beginning. So I want to address a couple of issues that Mx.
Buolamwini--did I say that right?
Mx. Buolamwini. Yes.
Ms. Horn. OK. And Ms. Whittaker especially have addressed
in turn. The first is the incorporation of bias into AI systems
that we are looking at more and more in our workplaces. This
isn't just a fun technological exercise. So, Mx. Buolamwini, in
your testimony you talked about inequity when it's put into the
algorithms and also the need to incorporate social sciences.
So my question to you is how do we create a system that
really addresses the groups that are most affected by this bias
that could be built into the code and identifying it in the
process? And then what would you suggest in terms of the
ability to redress that, how to identify it and address it?
Mx. Buolamwini. Absolutely. One thing I think we really
need to focus on is how we define expertise, and who we
consider the experts are generally not the people who are being
impacted by these systems. So looking at ways we can actually
work with marginalized communities during the design,
development, deployment but also governance of these systems,
so what--my community review panels that are part of the
process, that are in the stakeholder meetings when you're doing
things like algorithmic impact assessments and so forth, how do
we actually bring people in.
This is also why I suggested the public interest technology
clinics, right, because you're asking about how do we get to
redress? Well, you don't necessarily know how to redress the
issue you never saw, right? If you are denied the job, you
don't know. And so there needs to be a way where we actually
give people ways of reporting or connecting.
At the Algorithmic Justice League something we do is we
have ``bias in the wild'' stories. This is how I began to learn
about HireVue, which uses facial analysis and verbal and
nonverbal cues to inform emotional engagement or problem-
solving style. We got this notification from somebody who had
interviewed at a large tech company and only after the fact
found out that AI was used in the system in the first place.
This is something I've also asked the FTC (Federal Trade
Commission) about in terms of who do you go to when something
like this happens?
Ms. Horn. Thank you very much. And, Ms. Whittaker, I want
to turn to you. Several of the things that you have raised are
concerning in a number of ways. And it strikes me that we're
going to have to address this in a technological and social
sciences setting but also as a legislative body and a Congress,
setting some parameters around this that allow the development
but also do our best to anticipate and guard for the problems,
as you've mentioned.
So my question to you is, what would you suggest the role
or some potential solutions that Congress could consider to
take into account the challenges in workplace use of AI?
Ms. Whittaker. I want to emphasize my agreement with Mx.
Buolamwini's answer. I will also point to the AI Now
Institute's Algorithmic Impact Assessment Framework, which
provides a multi-step process for governance. The first step
involves reviewing the components that go into creating a given
AI system: examining what data informs the system, how the
system designed, and what incentives are driving the creation
and deployment of the system. The second involves examining the
context where the system is slated to be deployed, for instance
examining a workplace algorithm to understand whether it's
being used to extract more profit, whether it's being designed
in ways that protect labor rights, and asking how we measure
and assess such things. And the third and critical step is
engaging with the communities on the ground, who will bear the
consequences of exploitative and biased systems. These are the
people who will ultimately know how a given system is working
in practice. Engineers in a Silicon Valley office aren't going
to have this information. They don't build these systems to
collect such data. So it's imperative that oversight involve
both technical and policy expertise, and on-the-ground
expertise. And recognize that the experience of those on the
ground is often more important than the theories and
assumptions of those who design and deploy these systems.
Ms. Horn. Thank you. My time is expired. I yield back.
Chairwoman Johnson. Thank you very much. Ms. Stevens.
Ms. Stevens. Thank you, Madam Chair. Artificial
intelligence, societal and ethical implications, likely the
most important hearing taking place in this body today with
profound implications on our future and obviously our present-
day reality. Likely, the time we've allotted for this hearing
is not enough. In fact, it might just be the beginning.
We've referenced it before, our proverb behind us, ``Where
there is no vision, the people will perish.'' And this is
certainly an area where we need profound vision, a push toward
the implications. And something that Mx. Buolamwini's statement
in your testimony jumped out at me, which is that we have
arrived overconfident and underprepared for artificial
intelligence. And so I was wondering if each one of our
panelists could talk about how we--not just as legislators are
overconfident--in fact, I just think we're behind--but how we
are underprepared. Thank you.
Ms. Whittaker. Well, I think one of the reasons we're
overconfident is, as I said in my opening statement, that a lot
of what we learn about AI is marketing from companies who want
to sell it to us. This kind of marketing creates a lot of hype,
which manifests in claims that AI can solve complex social
problems, that its use can produce almost magical efficiencies,
that it can diagnose and even cure disease. And on and on.
But we're unprepared to examine and validate these systems
against these claims. We have no established, public mechanism
for ensuring that this tech actually does what the companies
selling it say it does. For the past two decades the tech
industry has been allowed to basically regulate itself. We've
allowed those in the business of selling technology to own the
future, assuming that what's good for the tech industry is good
for the future. And it's clear that this needs to end.
In our 2018 annual report, AI Now recommended that truth in
advertising laws be applied to AI technologies. All claims
about AI's capabilities need to be validated and proven, and if
you make a claim that can't be backed up, there will be
penalties. The fact that such regulation would fundamentally
change the way in which AI is designed and deployed should tell
us something about how urgently it's needed.
Mr. Clark. We're overconfident when it comes to believing
these systems are repeatable and reliable. And as the
testimonies have shown, that's repeatable for some, reliable
for some. That's an area where people typically get stuff
wrong.
As a society, we're underprepared because we're under-
oriented. We don't know where this technology is going. We
don't have granular data on how it's being developed. And the
data that we do have is born out of industry, which has its own
biases, so we need to build systems in government to let us
measure, assess, and forecast for this technology.
Mx. Buolamwini. First, I want to attribute Cathy O'Neil for
we've arrived in the age of automation overconfident. I added
underprepared because of all of the issues that I was seeing,
and I do think part of the overconfidence is the assumption
that good intentions will lead to a better outcome. And so
oftentimes, I hear people saying, well, we want to use AI for
good. And I ask do we even have good AI to begin with or are we
sending parachutes with holes?
When it comes to being underprepared, so much reliance on
data is part of why I use the term data is destiny, right? And
if our data is reflecting current power shadows, current
inequalities, we're destined to fail those who have already
been marginalized.
Dr. Tourassi. So what we covered today was very nicely the
hope, the hype, and the hard truth of AI. We covered every
aspect. And actually this is not new. The AI technologies that
existed in the 1990s, they went through the same wave. What's
different now is that we're moving a lot more--a lot faster
because of access to data and access to computer resources. And
there is no doubt that we will produce code much faster than we
can produce regulations and policies. This is the reality.
Therefore, I believe that investments, strategic
investments in R&D so that we can consistently and continuously
benchmark datasets that are available for development of AI
technology to capture biases to the extent that we can foresee
these biases and continue to--continuously benchmark AI
technology not only from the point of deployment but as a
quality control throughout its lifetime, that needs to be part
of our approach to the problem.
Ms. Stevens. Well, thank you so much. And for the record, I
just wanted to make note that earlier this year in this 116th
Congress, I had the privilege of joining my colleague from
Michigan, Congresswoman Brenda Lawrence, and our other
colleague, Congressman Ro Khanna, to introduce H.R. 153, which
supports the development of guidelines for the ethical
development of artificial intelligence. So it's a resolution,
but it's a step in that direction.
And certainly as this Committee continues to work with the
National Institute of Standards and Technology and all of your
fabulous expertise, we'll hopefully get to a good place. Thank
you.
I yield back, Madam Chair.
Chairwoman Johnson. Thank you very much.
That concludes our questioning period. And I want to remind
our witnesses that the record will remain open for 2 weeks for
any additional statements from you or Members or any additional
questions of the Committee.
The witnesses are now excused. I thank you profoundly for
being here today. And the hearing is adjourned.
[Whereupon, at 12:03 p.m., the Committee was adjourned.]
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