[House Hearing, 116 Congress]
[From the U.S. Government Publishing Office]
ARTIFICIAL INTELLIGENCE
AND THE FUTURE OF WORK
=======================================================================
HEARING
BEFORE THE
SUBCOMMITTEE ON RESEARCH AND TECHNOLOGY
COMMITTEE ON SCIENCE, SPACE, AND TECHNOLOGY
HOUSE OF REPRESENTATIVES
ONE HUNDRED SIXTEENTH CONGRESS
FIRST SESSION
__________
SEPTEMBER 24, 2019
__________
Serial No. 116-48
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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
37-740PDF 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
------
Subcommittee on Research and Technology
HON. HALEY STEVENS, Michigan, Chairwoman
DANIEL LIPINSKI, Illinois JIM BAIRD, Indiana, Ranking Member
MIKIE SHERRILL, New Jersey ROGER MARSHALL, Kansas
BRAD SHERMAN, California TROY BALDERSON, Ohio
PAUL TONKO, New York ANTHONY GONZALEZ, Ohio
BEN McADAMS, Utah JAIME HERRERA BEUTLER, Washington
STEVE COHEN, Tennessee
BILL FOSTER, Illinois
C O N T E N T S
September 24, 2019
Page
Hearing Charter.................................................. 2
Opening Statements
Statement by Representative Haley Stevens, Chairwoman,
Subcommittee on Research and Technology, Committee on Science,
Space, and Technology, U.S. House of Representatives........... 8
Written Statement............................................ 9
Statement by Representative Jim Baird, Ranking Member,
Subcommittee on Research and Technology, Committee on Science,
Space, and Technology, U.S. House of Representatives........... 67
Written Statement............................................ 68
Written statement by Representative Eddie Bernice Johnson,
Chairwoman, Committee on Science, Space, and Technology, U.S.
House of Representatives....................................... 69
Witnesses:
Dr. Arthur Lupia, Assistant Director, Directorate for Social,
Behavioral and Economic Sciences, National Science Foundation
Oral Statement............................................... 11
Written Statement............................................ 13
Dr. Erik Brynjolfsson, Schussel Family Professor of Management
Science and Director, The MIT Initiative on the Digital
Economy, Massachusetts Institute of Technology
Oral Statement............................................... 20
Written Statement............................................ 22
Ms. Rebekah Kowalski, Vice President, Manufacturing Services,
ManpowerGroup
Oral Statement............................................... 36
Written Statement............................................ 38
Dr. Sue Ellspermann, President, Ivy Tech Community College
Oral Statement............................................... 55
Written Statement............................................ 57
Discussion....................................................... 69
Appendix I: Answers to Post-Hearing Questions
Dr. Arthur Lupia, Assistant Director, Directorate for Social,
Behavioral and Economic Sciences, National Science Foundation.. 84
Appendix II: Additional Material for the Record
Letter submitted by Representative Haley Stevens, Chairwoman,
Subcommittee on Research and Technology, Committee on Science,
Space, and Technology, U.S. House of Representatives........... 86
Executive Summary submitted by Representative Haley Stevens,
Chairwoman, Subcommittee on Research and Technology, Committee
on Science, Space, and Technology, U.S. House of
Representatives................................................ 88
Statement submitted by Representative Haley Stevens, Chairwoman,
Subcommittee on Research and Technology, Committee on Science,
Space, and Technology, U.S. House of Representatives........... 100
ARTIFICIAL INTELLIGENCE
AND THE FUTURE OF WORK
----------
TUESDAY, SEPTEMBER 24, 2019
House of Representatives,
Subcommittee on Research and Technology,
Committee on Science, Space, and Technology,
Washington, D.C.
The Subcommittee met, pursuant to notice, at 4:02 p.m., in
room 2318 of the Rayburn House Office Building, Hon. Haley
Stevens [Chairwoman of the Subcommittee] presiding.
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairwoman Stevens. This hearing will come to order.
Without objection, the Chair is authorized to declare recess at
any time. Good afternoon. Welcome, and thank you to our
witnesses for joining us here today. We are all looking forward
to your testimony. Thank you also for your flexibility with the
later start this afternoon. I'd like to take a moment to offer
my deepest sympathies to Majority Whip Clyburn on the passing
of his beloved wife, Emily. My thoughts are with him and his
family during this time of sorrow.
We are here today to examine the role of artificial
intelligence in shaping the work of the future. Recent
developments in machine learning algorithms, combined with
increasing computing power and data generation, have enabled
rapid advances in the accuracy, efficiency, and applicability
of artificial intelligence (AI) systems. AI systems have
already begun to change the nature of work and the workforce.
They are being used in manufacturing processes, medical care,
and customer service.
As we talk--and we will talk about this--as we talk about
job loss that will occur as advanced technology increasingly
affects all occupations and wage levels, companies in my
district in southeastern Michigan are also telling me how much
trouble they are having trying to fill the jobs they have
available. A 2017 study by the McKinsey Global Institute found
that approximately half of all work activities could be
automated by technologies that are already available today, so
we need to start having the discussion at a broader level about
how available jobs will transform, rather than disappear, as
specific tasks are taken over by AI systems, and the workers
take on new job roles. The advances enabled by artificial
intelligence also have the potential to create new kinds of
jobs, and in doing so, elevate the standard of living and
quality of life for many.
Sixty-five percent of children entering elementary school
today, in the year 2019, will ultimately end up working in
completely new job types that currently do not exist. As the
integration of these technologies transform work and create new
jobs, there will be significant need to ensure we are training
workers to succeed at all levels, from the factory floor worker
to the radiologist. The key is ensuring that the gains from AI
systems are shared by all Americans, increasing the quality of
life for everyone. As we discussed at a hearing in this
Committee in June, if our Nation leads in the responsible
development of AI, we can help set the standards and norms the
rest of the world will follow. That applies equally to the use
of AI in the workplace.
We are holding this hearing today to discuss what we do
know, and also explore what we do not know, and the compelling
topic of the future of work has certainly compelled many.
Research studies, companies who are organizing and orienting
their organizational development, academic institutions, and
this very body, are compelled to act. As AI-powered robots
become more common, the question we ask is, how do we ensure
worker safety alongside these robots? Will artificial
intelligence be routinely used to monitor workers, as some
companies do today? How do we balance privacy rights with the
potential productivity benefits and worker benefits these
analyses could provide? How do we keep this data secure, and
prevent its malicious use? And finally, how do we get a better
understanding of the macroeconomics and labor outlook so that
the government, companies, colleges, universities, and workers
can all plan for this transition? It's the question hanging
above us in this 21st century age. These are just some of the
questions the researchers are pursuing.
So I am greatly looking forward to today's hearing, because
we are compelled to act, to explore, to develop good policy, to
stand up for the value of work, what knowledge and tools,
researchers, companies, and workers need going forward, and how
Federal science agencies, such as the NSF (National Science
Foundation), are helping to lead the way.
Before I recognize Dr. Marshall for an opening statement.
Wait, hold on 1 second. We're pausing on an opening statement.
OK. Before we move on for opening statements, what I'd like to
do at this time is to present for the record a letter from
Kelly Services in support of this hearing, and I would also
like to submit the executive summary from the 2018 report
written by the great Mark Muro, and his team from The Brookings
Institution, titled ``Automation and Artificial Intelligence:
How Machines Are Affecting People and Places'', a great read
that's recommended by many.
[The prepared statement of Chairwoman Stevens follows:]
Good afternoon, welcome and thank you to our witnesses for
joining us here today, I'm looking forward to hearing your
testimony. Thank you for your flexibility with the late start
today. I'd like to take a moment to offer my deepest sympathies
to Majority Whip Clyburn on the passing of his wife; my
thoughts are with him and his family during this time of
sorrow.
We are here today to examine the role of artificial
intelligence in shaping the work of the future. Recent
developments in machine learning algorithms, combined with
increasing computing power and data generation, have enabled
rapid advances in the accuracy, efficiency and applicability of
artificial intelligence systems.
AI systems have already begun to change the nature of work
and the workforce. They are being used in manufacturing
processes, medical care, and customer service.
As we talk about the job loss that will occur as advanced
technology increasingly affects all occupations and wage
levels, companies in my district are telling me about how much
trouble they are having trying to fill the jobs they have
available. A 2017 study by the McKinsey Global Institute found
that approximately half of all work activities could be
automated by technologies that are already available today.
We need to start having the discussion at a broader level
about how the types of jobs available will change rather than
disappear, as specific tasks are taken over by AI systems and
the workers take on new tasks.
The advances enabled by artificial intelligence also have
the potential to create new kinds of jobs, and in doing so,
elevate the standard of living and quality of life for many.
65% of children entering elementary school today will
ultimately end up working in completely new job types that
currently do not exist.
As the integration of these technologies changes jobs and
creates new jobs, there will be a significant need to ensure we
are training workers to succeed at all levels, from the factory
floor worker to the radiologist. The key is ensuring that the
gains from AI systems are shared by all Americans, increasing
the quality of life for everyone. As we discussed at a hearing
in this Committee in June, if our Nation leads in the
responsible development of AI, we can help set the standards
and norms the rest of the world will follow. That applies
equally to the use of AI in the workplace.
We are holding this hearing today to discuss what we do
know, but the fact is there is a lot we still do not know. As
AI-powered robots become more common, how do we ensure worker
safety alongside these robots? Will artificial intelligence be
routinely used to monitor workers, as some companies do today?
How do we balance privacy rights with the potential
productivity benefits and worker benefits these analyses could
provide? How can we keep this data secure and prevent its
malicious use? And finally, how do we get a better
understanding of the macroeconomics and labor outlook so that
the government, companies, colleges and universities, and
workers can all plan for the transition? These are just some of
the many questions researchers are pursuing.
I look forward to hearing from today's distinguished panel
who will help us understand what we do know now, what knowledge
and tools researchers, companies, and workers need going
forward, and how Federal science agencies such as NSF are
helping to lead the way.
Chairwoman Stevens. So at this time I would like to
introduce our witnesses. Our first witness is Dr. Arthur Lupia.
Dr. Lupia is the Assistant Director of the Directorate for
Social, Behavioral, and Economic Sciences at the National
Science Foundation. He also serves as the Hal R. Varian
Collegiate Professor of Political Science at the University of
Michigan. Delighted to have you here on behalf of the
University of Michigan, as well as the NSF, Dr. Lupia, and you
also serve as the co-Chair of the Office and Science and
Technology Policy's Subcommittee on Open Science. Dr. Lupia's
research focuses on processes, principles, and factors that
guide decisionmaking and learning. He earned his bachelor's
degree in economics from the University of Rochester, and his
social science Ph.D. from the California Institute of
Technology, Caltech.
Our next witness is Dr. Erik Brynjolfsson. Dr. Brynjolfsson
is the Schussel Family Professor of Management Science and
Director of the MIT Initiative on the Digital Economy. His
research focuses on the effects of information technologies on
business strategy, productivity and performance, digital
commerce, and intangible assets. He is the author and co-author
of several books, including ``The Second Machine Age: Work,
Progress, and Prosperity in a Time of Brilliant Technologies.''
We applaud you for this milestone work that you have published,
sir. We are delighted to have you here at this hearing, and we
also note that you received your bachelor's and master's
degrees in applied mathematics and decision sciences from
Harvard University, and a Ph.D. from MIT in managerial
economics.
Our third witness is Ms. Rebekah Kowalski. Ms. Kowalski is
the Vice President of Manpower Manufacturing, a role she has
held since January 2019 throughout her long and remarkable
career at ManpowerGroup. Her current portfolio focuses on
developing solutions that help organizations and leaders deal
with the implications of the shortage of skilled workers, and
the evolution of roles and skills. She previously led the team
that worked with MXD, a digital manufacturing institute, to
identify how roles and skills will evolve as manufacturing
changes with the increasing introduction of digital
technologies, a truly profound work of primary research that
has helped many companies orient and prepare for the future of
work. Ms. Kowalski received her B.A. in English from the
University of Wisconsin-Parkside.
Our final witness, Dr. Sue Ellspermann, is the President of
Ivy Tech Community College of Indiana. Prior to her role at Ivy
Tech, Dr. Ellspermann was Indiana's 50th Lieutenant Governor,
from 2013 to March 2016. As Vice Chair of the Indiana Career
Council, she led efforts to align the State's education and
workforce development system to meet the needs of employers, a
continued focus for her as President of Ivy Tech. She certainly
focuses on the cross-cutting collaboration that is so needed
with our training centers and our employers. And Dr.
Ellspermann earned her bachelor's of science in industrial
engineering from Purdue University, and her master's of science
and Ph.D. in industrial engineering from the University of
Louisville. Absolutely fabulous.
As our witnesses should know, you will each have 5 minutes
for your spoken testimony, and your written testimony will be
included in the record for the hearing. When all of you have
completed your spoken testimony, we will begin with questions.
Members will have 5 minutes to question the panel. And at this
time, Dr. Lupia, we'd like to start with your 5-minute
testimony. Thank you.
TESTIMONY OF DR. ARTHUR LUPIA,
ASSISTANT DIRECTOR, DIRECTORATE FOR SOCIAL,
BEHAVIORAL AND ECONOMIC SCIENCES,
NATIONAL SCIENCE FOUNDATION
Dr. Lupia. Thank you. Good afternoon, Chairwoman Stevens,
Representative Marshall, and Members of the Subcommittee. My
name is Dr. Arthur Lupia. I am the Assistant Director of the
Social, Behavioral, and Economic Sciences Directorate at the
National Science Foundation. It is a pleasure to be with you
this afternoon to discuss how NSF is helping our fellow
citizens prepare for the future of work.
Work is a vital and dynamic element of our society. Work
powers our offices and our factories. It supports our
communities, and our Nation. And as we can all see, work is
changing. We know that AI and related technologies can increase
national competitiveness by making businesses, governments, and
social organizations more competitive and more effective. These
technologies can also create many new careers. If these
technologies are applied with sufficient foresight, they can
create new opportunities for workers, and improve quality of
life for communities across the country.
How can we achieve a future where technological change
benefits as many people as possible? At the National Science
Foundation, we believe that achieving this future requires
working together. Our Future of Work at the Human Technology
Frontier Program treats future work, future technology, and
future workplaces as deeply integrated and intertwined elements
of our Nation's work-based ecosystem. In NSF's Future of Work
approach, we collect data on worker experiences to inform
social and behavioral research on workers and workplaces. This
research, in turn, can guide technological development. Work
like this can reveal new ways to empower workers, and increase
productivity.
Studying workers, workplaces, and technology together are
the key to creating benefits that everyone can realize, and
pioneering research of this kind is already underway. On the
screen is one of the projects NSF has recently supported. This
is a human being in an exoskeleton. Today's exoskeletons help
human beings transport very large objects, and navigate
impossible situations. But this project is about tomorrow's
exoskeletons. The device that you see here is not just an
exoskeleton of the body. It's an exoskeleton of the mind. This
exoskeleton of tomorrow provides information to the worker
through an augmented reality system. The system empowers the
worker to process information, and make better decisions, with
unprecedented speed. This type of technology is awesome, and
it'll have impacts far beyond factory floors. Today, for
example, the Veterans' Administration is one of the Nation's
leading users of exoskeletons. Tomorrow's exoskeletons will
open new opportunities for our veterans.
NSF's Future of Work Program supports this technology by
incentivizing developers, AI experts, and workplace specialists
to collaborate. Working together, researchers and developers
can increase performance, decrease injury, expand access, and
improve quality of life in ways that just would not be possible
if any of these groups worked alone. That's what NSF can do. To
date, NSF's Future of Work Big Idea supports projects in a wide
range of work contexts, including health care, power grids,
farming, learning, scientific research, transportation,
emergency response, and, of course, manufacturing.
NSF not only supports fundamental research in these areas,
but also supports efforts to bring these big ideas to market.
For example, NSF recently unveiled new Future of Work awards
from its Convergence Accelerator. NSF's Convergence Accelerator
is designed to fund technology-based partnerships that
simultaneously advance national priorities and create new
opportunities for American workers. For example, a project
based at the University of Michigan is examining how to combine
research in AI, data science, and industrial psychology to find
better ways to link workers with innovative new training and
educational opportunities that will help them not only
contribute, but thrive, and build amazing careers in their new
workplaces.
This is an exciting time for our country, and, like you,
NSF is grateful to see our Nation's brightest minds
collaborating on the fundamental research that will transform
our workplaces, empower our workforce, and provide tremendous
new sources of innovation for our Nation. So thank you for
having this hearing today, and for the opportunity to testify.
I'm happy to answer any questions you may have.
[The prepared statement of Dr. Lupia follows:]
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Chairwoman Stevens. Dr. Brynjolfsson? Yes.
TESTIMONY OF DR. ERIK BRYNJOLFSSON,
SCHUSSEL FAMILY PROFESSOR OF MANAGEMENT
SCIENCE AND DIRECTOR, THE MIT INITIATIVE
ON THE DIGITAL ECONOMY,
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
Dr. Brynjolfsson. Good afternoon, Chairwoman Stevens,
Representative Marshall, and Members of the Committee. Thank
you so much for inviting me to share some of the research my
team and I have been doing. Addressing the opportunities
created by AI is one of the most important challenges for
government in the coming decade. Thanks to AI, some weird and
wonderful things are beginning to happen. Cars are learning to
drive themselves. Machines can now recognize your friends'
faces. When you see people walking down the street talking on
their phones, you don't know if they're talking to another
human or to a machine, and expecting the machine to answer.
Just last week Siri tried to join into a conversation I was
having about interest rates.
However, it's also critical to understand that we are very
far from what we call artificial general intelligence, the kind
of AI that spans the full range of human intelligence. While
machine learning is now superhuman in many tasks that involve
mapping a particular set of inputs into outputs, humans
outperform machines in most other cognitive tasks. Therefore,
we are not facing the imminent end of work, but we are facing a
major restructuring of work. In research that I've been doing
with my colleagues, we find that few, if any, occupations will
be fully automated by this new wave of technologies, but at the
same time, few, if any, will be unaffected. Instead, most will
be transformed. For instance, the job of a typical radiologist
consists of 27 distinct tasks. While machine learning has made
impressive advances in some of them, like reading medical
images, it is of little use in most of the other tasks, like
counseling patients.
So massive unemployment is not the challenge of our era.
Instead, we face challenges in two other areas. One is
delivering productivity growth, and the other is reducing
inequality. To date, despite impressive improvements in AI,
productivity growth has actually slowed down. Between 1995 and
2004 it averaged 2.8 percent per year, but since 2005
productivity has been just 1.3 percent per year. That's less
than half the growth rate previously. So why is that? Well, the
bottleneck is actually not the technology, but rather the lack
of complementary process innovations, workforce reskilling, and
business dynamism.
The second challenge is inequality. There's no economic law
that says that everyone will benefit from technological
advances. As the economic pie grows, it's possible for some
people to be left behind, even as others benefit
disproportionately. Indeed, over the past several decades the
benefits of economic growth have been very unequal. Not only
has the median income barely grown since the late 1990s, but
other social indicators have actually worsened. For the first
time in history, average life expectancy of Americans has begun
to fall, driven by worse mortality of less educated Americans.
It's no coincidence that these are exactly the Americans who
haven't shared in our economic growth.
So my policy recommendations can be grouped into five key
areas. The first one is to reinvent education. We need to
recommit ourselves to investment in education. It's a field
that the U.S. has once led the world. We also need to reinvent
it so that we focus more on the kinds of skills that machines
cannot match. These include creativity and interpersonal
skills.
Second, we need to rebalance capital and labor. As noted in
a recent research report by the MIT Work of the Future
Initiative, our tax code and other policies are heavily skewed
toward helping capital, rather than labor. We need a more-level
playing field, particularly as AI starts to affect more and
more of the labor force. This means taxing capital at
comparable rates, encouraging investments in human capital,
just as we do for physical capital, and updating corporate
governance to recognize workers as stakeholders alongside
stockholders. We can also expand the Earned Income Tax Credit
to boost incomes for the working poor, and use revenues from
things like carbon taxes to lower taxes on work.
Third, we need to invest in U.S. technological leadership.
U.S. leadership in AI and other technologies is at serious risk
because we have cut Federal investment in R&D (research and
development), even as other nations have boosted theirs.
Federal science agencies, like the NSF, working with our
leading universities and private industry, have a central role
in maintaining and extending America's science and technology
leadership in AI and other areas.
Fourth, we need to welcome high skill immigrants. A vastly
disproportionate number of America's leaders in science and
business are immigrants, or the children of immigrants. When I
ask my students at MIT what was the most important message I
should convey to you here in Washington regarding AI policy,
they unanimously advised me to push for less restrictive
immigration policies.
And fifth, we need to work hard to support
entrepreneurship. Boosting entrepreneurship can help reverse
the stagnation of wages for the bottom half of the income
distribution, particularly those who have been most adverse
affected by automation. Among the policies that can help with
this is decoupling healthcare from employment, reforming
occupational licensing, and direct investments in teaching and
entrepreneurship, and boosting new business formation.
With the right policies, AI can be harnessed to make the
next decade the best decade in U.S. history.
[The prepared statement of Dr. Brynjolfsson follows:]
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TESTIMONY OF REBEKAH KOWALSKI,
VICE PRESIDENT, MANUFACTURING SERVICES,
MANPOWERGROUP
Ms. Kowalski. Chairwoman Stevens, Ranking Member Dr. Baird,
and Representative Marshall, on behalf of ManpowerGroup, thanks
for the invitation to speak today on such an incredibly
important topic. ManpowerGroup is the world leader in
innovative workforce solutions. Every day we connect more than
600,000 people to work around the world in a wide range of
skills and industries. One of our most predominant industry
sectors is the manufacturing sector, and I oversee our
manufacturing solutions practice. I've worked with a lot of
companies as they are struggling to deal with the twin
challenge of finding enough rightly skilled talent, and
figuring out how they're going to navigate the bright new
future that digital offers.
Manufacturers are reporting talent shortages as they
struggle to find the right blend of technical and soft skills.
Our perspective is that AI, machine learning, and other digital
technologies produce new jobs that require new skills. Some of
those we can't even imagine yet. Our research shows that over
90 percent of employers expect to be impacted by digitization
over the next 2 years. Eighty-seven percent of them plan to
maintain or increase head count. Four percent say they don't
know. And yes, there is a small number, 9 percent of them, that
say that they anticipate a reduction. Fully 75 percent say this
is going to require new skills, skills that we do not currently
have in our workforce, and skills that we can't actually even
anticipate.
In 2017 we released a study with MXD, which was formerly
known as the Digital, Manufacturing, and Design Innovation
Institute, on how digital technologies, including AI and
machine learning, would impact manufacturing jobs. The study
was accomplished in partnership with academia and industry, and
identified 165 new or significantly evolved roles. Today the
majority of manufacturing roles are in the general entry level
population, by count. That is--those are roles like picker/
packer, assembler, operator, helper, laborer. And the
manufacturing sector, the backdrop here, is that we are going
to produce 3.5 million new jobs over the next decade, while at
the same time 2.7 manufacturing workers are set to retire. Many
of the new jobs will be in these more specialized areas, like
technicians, testers, analysts, specialists, and that's a
significant shift for us.
We have the following concerns. First, employers are
uncertain about how digitization will impact roles and skills,
and over what period of time. Second, the ability of employers
of all sizes to invest in upscaling falls far short of what is
required to produce the workforce they need, both from a time
and resource perspective. Third, the talent shortage impacts
all types of talent, from entry level to leadership, meaning
employers have to determine the best way to allocate precious
dollars. That disproportionately impacts small and mid-sized
manufacturers.
There are several obstacles to being resourceful around
talent attraction and upscaling. One, it's difficult for
organizations to predict workforce needs more than a year in
advance. Strategic workforce planning does not have as long of
a horizon as it needs. Without enough exact match talent, we
need to shift to hiring on potential and learnability, but H.R.
(human resources) systems and processes are still geared toward
finding an exact match. Third, job descriptions need to be less
stationary, and more evolutionary, so that individuals can
actually anticipate the need for ongoing learning and
adaptation. And four, organizations lack sufficient funding for
workforce training.
An example of improved training processes is what we do
with Rockwell Automation in our Academy of Advanced
Manufacturing, where we take veterans and we put them through a
12-week embedded program, and we graduate them as Certified
Automation Technicians. They walk away with a job that, on
average, is double what they were making when they came in, and
the employer walks away with the talent that they need. With 12
million manufacturing workers in the U.S., we need those kinds
of nimble programs, many, many more of them, in order to ensure
that people have a path to sustainable prosperity, and we need
to start now. Don't count the humans out.
Talent is, in fact, the most renewable resource we have on
the planet. It is ready to learn, adapt, and thrive in new
environments, and we need to work collectively now across
educators, employers, and individuals to become proactive
builders of talent to develop a workforce with the skills
employers and individuals need to remain competitive, both now
and in the future.
Thank you again to the Subcommittee for the opportunity to
share my testimony.
[The prepared statement of Ms. Kowalski follows:]
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TESTIMONY OF DR. SUE ELLSPERMANN,
PRESIDENT, IVY TECH COMMUNITY COLLEGE
Dr. Ellspermann. Thank you, Chairwoman Stevens, Ranking
Member Baird, welcome, and Representative Marshall. It's really
a privilege to be here representing community colleges today,
and Ivy Tech Community College specifically, as we talk through
machine learning, artificial intelligence, and particularly how
that's affecting community colleges, and how we're working with
industry and businesses to establish an ecosystem to address
the changing demands. I also will speak at the end about what
the Federal Government could do to assist in this work.
So remember that community colleges are the most common
type of U.S. college, with Ivy Tech being one of those,
established in 1963 as a vocational/technical college, now the
largest in the Nation Statewide system, singly accredited, with
150,000 students and 18 campuses, 40 locations. But think about
our student, who is now--the traditional student is that
community college-like student, who is part time. Average age
is probably 27 years old, Pell eligible, and a quarter of those
students have dependents, children, that is, and you can see
more in our report.
But how will that impact us as we look at AI and machine
learning? And what you heard from several of my colleagues here
is that there will be some displacement, but with that
displacement will become very good opportunities, and it's up
to our community colleges to prepare those students, those
employees, for the wide spectrum of industries and
opportunities that are out there. So let me talk about just a
few of the very concrete things that we've done, and I thank
Ms. Kowalski for sharing some of those as well in the
manufacturing space, but one that I'm sure she'd be interested
in is the partnership that Ivy Tech's done with the Smart
Automation Certification Alliance as they've developed the
first certifications in industry 4.0, which we know will be
factories of the future, and the kind of credentials we'll need
in that very connected manufacturing environment.
But at the community college level, we work with many
partners, for instance, Sales Force, through their Pathfinders
Program to earn Sales Force developer and administrator
certifications. We have many certificates in informatics and
software development at the Associate level. We work with Apple
in their iOS systems applications. We work with Cisco, as they
overhaul their certifications, to embed those right into our IT
programs. With Amazon Web Services, we are developing cloud
computing certificates, and soon to be an Applied Associate in
Cloud Computing. All of those are staying with those industries
and particular businesses to make sure that we're providing our
students with the kind of skills that they will need.
I'm going to speak to a partnership we have with industry,
particularly our Achieve Your Degree Program, which is a
redesign of the tuition reimbursement program, where industries
actually pay for, at the end of that cycle, the tuition that
that employee of theirs pursues, but we, concierge-style, come
to the industry, that business, to enroll, to do financial aid
eligibility, and then to ensure that the programs align with
what the business has. In doing that, we've had a great
partnership with our Indiana Chamber of Commerce, Statewide,
more than 200 companies doing that. I'll just share one, with
Cook Group in Bloomington, Indiana, where 500 of their
employees are being skilled up, have already earned 100
credentials in the last 3 years.
Now, in design, we put everything, from an economist
standpoint, into quadrants to make sure that the highest demand
areas with the smallest supply of employees are being built up
into those particular quadrants. We'll describe those quadrants
more in our full report, but in doing that, we make sure that
we are putting our focused energy in the high-demand areas,
that we're shrinking problems that need to shrink, and that we
are seeking equilibrium in this highly changing environment.
And it's working. In IT we, just last year, increased our
completions by 75 percent in a single year, and we see that
across our programs.
I'm going to spend my last moments talking about what we
could do with some Federal support. You know, employers hate to
have to pay Unemployment Insurance (UI) into that trust fund.
Several years ago, most of our States were in a deficit. We
were in Indiana. Congressman Baird remembers that. Today we are
at $900 million in the black. Those funds could be deployed
toward this work rescaling earlier than when that person is
displaced, but when you decide on that technology, and we're
hopeful that there will be some willingness of this Congress to
look at making that available to a State and a community
college system to experiment with how we could deploy a portion
of those UI funds in these ways. We look for all kinds of
support in reducing regulation so that we can change at the
speed of the technologies that we're working with to ensure
that all of our workers have those opportunities. And with
that, I'll just thank you for the opportunity for appearing
before the Subcommittee, and the opportunity to share the work
of Ivy Tech Community College.
[The prepared statement of Dr. Ellspermann follows:]
[GRAPHICS NOT AVAILABLE IN TIFF FORMAT]
Chairwoman Stevens. Well, thank you all, and at this time
the Chair would like to recognize Ranking Member Dr. Baird for
his opening remarks. Thank you.
Mr. Baird. Thank you, Madam Chairwoman. I apologize for
being late, but I do admire you for going on without me. Thank
you.
Chairwoman Stevens. We're a team, you know.
Mr. Baird. So I appreciate this opportunity. I appreciate
you waiting on me, and I'd like to make this opening statement,
and thank you for holding this ``Artificial Intelligence and
the Future of Work'' Committee hearing. Since the term AI was
introduced in the 1950s, we have made some huge advances in the
field, and thanks to critical investments by government and
industry, universities and the United States, in leading global
AI research and development.
Today AI systems have been deployed in every sector of the
U.S. economy. These technologies have already delivered
significant benefits for the U.S. economic prosperity, for the
environmental stewardship, and the national security. AI has
long been a subject of interest of the House Science Committee,
and we have held several important and productive hearings on
this topic. In the past we have discussed how to define AI, the
science of AI technologies, and the needs for standards to
address ethics and potential bias. Now, this afternoon, we will
examine AI from the prospective of the American worker.
In order to remain a leader in AI, I believe we must
prepare our workforce for the next generation of opportunities
in this technology, and for our future, defined by a lifelong
learning experience. In order to grow our economy, I also
believe we must acknowledge and understand how AI is changing,
and will continue to change, the jobs and lives of hard-working
Americans. This is a large scale effort that is going to
require cooperation between industry that was already mentioned
here, industry, academia, and the Federal agencies, so I'm
pleased to see that the Trump Administration is making this
issue a priority, and recently established the National Science
Council for the American Worker and the American Workforce
Policy Advisory Board. American industry has responded well to
the Administration's initiatives. Over 300 companies and
organizations have pledged to study and expand education,
training, re-skilling opportunities for American workers to
gain AI-relevant skills.
We also need to re-think how we educate future workers, and
re-skill the workers of today, all the way from K through 12
schools to the community colleges, the vocational schools, and
the 4-year universities. Some leaders in the U.S. education
system are already finding innovative ways to develop a highly
skilled AI workforce, one of the future. We have heard about
some of those efforts from my friend, Dr. Sue Ellspermann,
President of the Ivy Tech Community College system in our home
State of Indiana. Sue, so glad to have you here today. At Ivy
Tech, Dr. Ellspermann works to address the changing demands of
employers in the Hoosier State by providing strategic support
and career planning for students at community colleges and
vocational schools, and working closely with local industry. I
look forward to hearing more about her important work in our
community, and how it will be applied across the country.
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 work together on AI
challenges, including today's critical workforce 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 appreciate the opportunity to hear how this Committee and the
Federal Government can support innovation and education to
ensure a bright future for America's workers, our students, and
maintain our leadership in AI. So thank you.
[The prepared statement of Mr. Baird follows:]
Chairwoman Stevens, thank you for holding today's hearing
on ``Artificial intelligence (AI) and the Future of Work.''
Since the term AI was first coined in the 1950s, we have
made huge advances in the field. And thanks to critical
investments by government, industry, and universities, the
United States is leading in global AI Research & Development.
Today, AI systems have been deployed in every sector of the
U.S. economy. These technologies have already delivered
significant benefits for U.S. economic prosperity,
environmental stewardship, and national security.
AI has long been a subject of interest for the House
Science Committee and we have held several important and
productive hearings on this topic.
In the past, we have discussed how to define AI, the
science of AI technologies, and the needs for standards to
address ethics and potential bias.
Now, this afternoon, we will examine AI from the
perspective of the American worker.
In order to remain a leader in AI, I believe we must
prepare our workforce for next generation opportunities in this
technology and for a future defined by lifelong learning.
In order to grow our economy, I also believe we must
acknowledge and understand how AI is changing and will continue
to change the jobs and lives of hard-working Americans.
This is a large-scale effort that is going to require
cooperation between industry, academia and federal agencies.
So I am pleased to see that The Trump Administration is
making this issue a priority and recently established the
National Council for the American Worker and the American
Workforce Policy Advisory Board.
American industry has responded well to the
Administration's initiatives. Over 300 companies and
organizations have pledged to study and expand education,
training, and reskilling opportunities for American workers to
gain AI-relevant skills.
We also need to rethink how we educate future workers and
reskill the works of today, all the way from K-12 schools to
community colleges and vocational schools, to 4-year
universities.
Some leaders in the U.S. education system are already
finding innovative ways to develop a highly-skilled AI
workforce of the future.
We will learn more about some of those efforts from one of
our witnesses today, my good friend, Dr. Sue Ellspermann,
President of the Ivy Tech Community College system in our home
state of Indiana.
At Ivy Tech, Dr. Ellspermann works to address the changing
demands of employers in the Hoosier State by providing
strategic support and career planning for students at community
colleges and vocational schools and working closely with local
industry.
I look forward to hearing more about her important work in
our community, and how it can be applied across the country.
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 work together on AI challenges,
including today's critical workforce 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.
I look forward to hearing how we can support innovation and
education, to ensure a bright future for America's workers and
students and maintain our leadership in AI.
Chairman Stevens. 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 Chairwoman Johnson follows:]
Thank you, Chairwoman Stevens and Ranking Member Baird, for
holding this hearing. I would also like to welcome this
esteemed panel of witnesses and thank each of you for
accommodating the rescheduling of today's hearing. We are here
today to discuss an urgent challenge facing the country.
Artificial intelligence is a rapidly advancing, sophisticated
technology that promises to transform the way we live and work.
As Chairwoman, I take seriously the responsibility
entrusted to this Committee to support the nation's research
and innovation enterprise for the benefit of society. We are
increasingly feeling pressure from our global competitors,
particularly in the case of AI. As countries like the United
Kingdom, Germany, and China invest heavily in this technology,
there is a strong sense of urgency to race headlong toward
technological maturity and widespread adoption.
I want to urge caution. We must take the time to draw upon
lessons learned from past technological disruptions, assess the
opportunities and potential risks, and implement a coordinated
national strategy to ensure the benefits of AI are enjoyed by
everyone. We are here to explore one of the primary concerns
associated with AI - its potential impact on the workforce.
Many Americans are understandably worried that AI-driven
automation and robots will make their jobs obsolete.
Research has a critical role to play in informing how AI is
integrated into the American workforce. Research can help
employers understand the benefits and risks of this technology.
Just because it seems like a task can be performed by an AI
system, does not mean it can or should be, at least not without
a human still in the loop. Research can also improve our
understanding of the human-technology relationship. This can
inform decisions regarding how best to integrate AI into the
workflow so it can both complement and enhance the value of the
worker. Research can advance the development of effective
practices for retraining the current workforce and for ensuring
workers have the flexibility to be lifelong learners. Research
can provide students and those pursuing a career change with a
clear understanding of emerging industries and occupations, so
they can chart an education path best suited to their goals.
Artificial intelligence holds immense promise to spur
economic growth and make our lives easier. We are at a critical
point in the development of this technology, and we must ensure
we have the research knowledge base necessary to maximize these
benefits for everyone.
I look forward to today's testimony and discussion and I
yield back.
Chairwoman Stevens. Fabulous. At this time we're going to
begin the 5-minutes of questioning, and the Chair will
recognize herself for 5 minutes.
Dr. Lupia, in your testimony, you discuss a recent award
made to the University of Michigan to support research on how
humans and robots are working together in construction
environments, and you stated that, despite recent advances in
robot functionality, many fundamental questions in robot
interaction remain unanswered. Do you mind elaborating on that
a little bit further, and also, could you touch on some of the
major social science research questions regarding human/robot
interaction, and where we need to go from here?
Dr. Lupia. Thank you for that question. As discussed in the
opening statements, there are things right now that AI and
robots can do that humans can't do, but there are many things
that humans can do that robots can't do. And when we're
thinking about the workplace of the future, particularly its
impact on workers and workplaces, you know, there are these
fundamental questions about what the two groups know now, and
what can we expect them to know in the future, to empower
workers.
So I think about farms, for example, right? So I grew up on
a farm, and so, when I was a kid, people milked cows. And, if
you've ever done that, it's not the most fun thing. But now
they have robots that can milk cows, so if you think about
how--just--a farm that's pretty simple, there are things that
people can do that robots can't do, and things that robots can
do that people can't do. And so, through a number of grants,
we're trying to help factories, farmers, offices, and so forth
think through, ``How do you make workplaces more efficient?''
``How do you make them more effective, with this set of
evolving skills?''
Some of it requires trust, right? So if we're going to
automate a manufacturing process, the worker has to trust the
robot, or the machine. And trust is a great thing, unless the
robot's about to do the wrong thing. And so you've always got
to have an override capacity. What we're trying to do at NSF is
bring large groups of people together to understand, at a
pretty fundamental level, when is the trust relationship going
to work, when is it going to fail, and as robots get better at
things, how does that change how we should organize the
workplace? So that's the fundamental question.
It takes understanding humans, because if you press the
override button at the wrong time, you can disrupt the process.
If you wait too long, unintended consequences can happen. So
understanding the human/robot interaction is really critical to
all of the progress we want, from manufacturing, to farms, to
offices of the future.
Chairwoman Stevens. To be successful. And, Ms. Kowalski,
you might have given us the line of the day, which is don't
count the humans out. And you also, in your testimony,
discussed the rapid change in skills being sought by employers.
And, you know, in terms of how we think about job descriptions
to account for this rapidly changing marketplace for skills,
and also promote a mindset of supportive, continuous skill
development, how do we do it all? How do we bring that
together?
Ms. Kowalski. So I think there's a few things. One is just
determining that this is what we have to do, right? It's a
decision that we have to make, that we cannot allow the
workforce to stay still, that there is no grassy plateau on
which we'll all be able to stretch out when transformation is
done. It will be an unending climb, and evolution and
adaptation, which we're very good at as human beings, right?
But the way that we approached education and employment was we
educated to the job. People came into an exact match
environment, and then they made progressions up the ladder
based on merit. We haven't seen something come in that acts so
rapidly.
Think about automation, and the--it was about 15 years
playing out in the last cycle. We're talking about something
that's going to play out, by this research, in 3 to 5 years
that's unfolding now. And it will get faster, and the peaks and
troughs will get steeper, and so how we get people attenuated
to that shift, that starts all the way back in K-12, and moves
all the way through--and in employment. And the hardest thing
is going to be taking the people that are currently employed
and helping them understand they haven't done anything wrong.
They are hardworking, they've been doing a great job, and these
are the new set of skills that they have to assimilate, and
there has to be a new contract, right? And that contract is one
of you put in for continuous adaptation and evolution, we'll be
right there to meet you with the resources.
You know when we were good at doing that? Was in the 1950s
and 1960s, when we hired on potential. We built whole companies
hiring on potential for jobs we didn't even know what they were
going to look like, and people got used to making the
progression, and having a partnership with employment and
educators in order to do that. And it's going to take a system
to do it.
Chairwoman Stevens. Well, I am just at time, so--you can
tell we're in a rich topic area. So I'm going to yield back the
remainder of my time, and I am going to recognize Dr. Baird for
5 minutes of questioning. You've got this, Dr. Baird.
Mr. Baird. Thank you, Madam Chair. And, Dr. Ellspermann,
I'm sure that you recognized I was probably going to start with
you.
Dr. Ellspermann. Thank you.
Mr. Baird. The thing that, and I know you spent a lot of
time in this area, and thinking about it, but the needs of the
industry today, compared to the future, and this technology
that we're discussing today, is changing so fast because of
quantum computing, and that sort of thing. So I guess, in other
words, how do you feel, or how do you see Ivy Tech balancing
that need for today, and then in the future? Kind of give us
some feel what you think that might look----
Dr. Ellspermann. Very good. So, actually, 3 years ago, the
General Assembly in Indiana understood how important it would
be that we would be work-forced aligned as a system, and
actually required that, in addition to having a provost I have
a Chief Workforce Officer, which makes sure this alignment
happened.
So I alluded in my comments to this way that we classify
all of our programs, because we know it's a moving target, we
know that there is BLS (Bureau of Labor Statistics), and Emsi,
and other good data out there, economically, to project the
future. We know that broadly, but it's not accurate at the
local level, so we take that, and we let the local industry
work with us to look at what's coming, what is the real demand,
and then we size our programs on every campus, every program,
to be that right size.
And that's where our quadrants, that quadrant one of--
quadrant one is where we focus. It is those high-demand, low-
supply, not enough students to fill that work, and making sure
we're building those programs. We have limited enrollment
programs that we have to push on. We have programs that have to
shrink so that they are the right size, or maybe discontinue,
and then finally equilibrium. That work is working. It is
working across our State. We can take the local data to
understand that maybe the economic data is not quite accurate
to what the local needs are, and we could shore up, and we
could shrink, and we do that in a very rapid way.
What becomes challenging is the support at the Federal
level to get those kind of programs, when you need new programs
stood up, to quickly stand those up in 3 to 6 months so that an
employer gets the kind of skill set that they need. And so we
look to any support we can get with our U.S. DOE (Department of
Education) to quickly approve programs. But, as I shared,
looking at new ways to anticipate, when we know these changes
are coming, and we know there's a higher demand, how do we
identify that employee at risk early, even if it is just a
year, or a year and a half in advance? We can then begin
skilling before that individual is out of a job, unemployed,
which is, for many, much more than just about being out of a
job. It is psychological impact. It is a feeling that a trust
has been broken with that employer, so how do we proactively
work with them?
And whether we use Unemployment Insurance as a part of that
trigger, we need to change that mindset to create that contract
again between employer and employee. And I believe the
community colleges, at least Ivy Tech, is working very hard to
get there, but I know we will be the front lines for most of
employers as they look to scale up their employees, and it's
our job to be as rapid as we can.
Mr. Baird. Thank you. Dr. Lupia, I'm glad to hear you came
from a farm, and I couldn't help but say--several of you
mentioned the importance of the human factor. I think you
mentioned that. But I just want you to know that those old cows
have a vested interest in how well these--but anyway, I thought
maybe you might want to elaborate--I've been fascinated by
NSF's convergence accelerators since Director Cordova spoke
about them in this Committee in May. Would you mind elaborating
on how this new approach to research will improve our
understanding of the future of work, and enhance the lives of
American workers?
Dr. Lupia. Absolutely, sir. Thank you for asking that
question. The convergence accelerators really build on the
traditional NSF approach. So in NSF, we fund all of science,
but the idea with the Convergence Accelerators is, from the
beginning, you bring in other partners, people in the room,
who, if great ideas emerge, they can bring them to market. So
the Convergence Accelerators have really been an exciting way
to think about how to take amazing collaborations and bring
them to market.
So I'll give you one example, because we just started
funding these things. One has to do with re-skilling the
workforce, and it is funded, coincidentally, at Purdue
University, and it focuses on apprenticeships. So both of my
grandparents were in the trades, and the way that you've
learned a trade for 100 years is through an apprenticeship. So
that takes a number of months, and you follow somebody around,
and you learn the trade. But for a small company it's really
expensive to take one of your workers and have them do an
apprenticeship for 6 months. So one of the Convergence
Accelerators is a project built around using technology to do
apprenticeships at scale.
So imagine we could take what a master plumber knows, or a
master technician, or someone who runs a computer, and we can
follow them around, and then create scalable, low-cost ways to
distribute this information to everybody. And one of the ways
you can do that is through having simulations. So instead of
one person shadowing the expert, you can build a simulation
where 50 people can have a virtual reality experience of
shadowing the expert. And so it has a lot of the benefits of
the traditional apprenticeship, and of course you still need
the one-to-one contact, but this is a way to really make that
happen at scale. And, again, if you're a small company, if you
can go to a community college, or somewhere else, and get this
type of training, it's a real game changer.
So the idea here is apprenticeship, lower costs, improve
speed and reliability, minimize errors, and this is something
that the Converge Accelerator, I think, can really do to help
companies across the country.
Mr. Baird. Thank you.
Chairwoman Stevens. At this time the Chair would like to
recognize Dr. Marshall for 5 minutes of questioning.
Mr. Marshall. All right. Thank you, Chairwoman. Dr.
Ellspermann, I'm a community college graduate, my wife,
community college graduate, huge fans of my community colleges.
The technical colleges can quickly pivot to the job needs of my
community, and I think that's where the rubber meets the road.
How do you measure success? What are you measuring to say,
we're being successful in our technical college that you run?
Dr. Ellspermann. We measure success the way most Americans
do, by wages. We actually measure the wages of our graduates 1
year out to see what they're making. Our goal is that 80
percent of all of our graduates will make above median wage 1
year after completion. We're at 45 percent today, we were at 38
percent 2 years ago, and we're marching our way--but we think
that is one fair way to do that. In addition, too, we also hold
ourselves accountable to those four quadrants. We want 80
percent of our programs to be in equilibrium, meaning we're
roughly producing the number of graduates needed for our
community.
Mr. Marshall. And are you measuring their debt when they're
leaving too?
Dr. Ellspermann. We have minimal, you know, community
college debt is kind of the best kind of debt. It's, like,
under $10,000. It's the way to do college. But we do measure
debt, and we do measure what those students have, and always
are looking for ways to continue to reduce that.
Mr. Marshall. I'm not sure how long you've been at Ivy
Tech, but what are you doing differently today than 1 year ago,
than 3 years ago, or 5 years ago?
Dr. Ellspermann. We have reinvented how we're delivering.
So we've gone from traditional 16-week courses to 8-week
courses because, guess what, adults do better in that format.
There's higher pass rates, lower drop rates. We've just
redesigned our online education. We're one of the largest
online educators in the country. We know we have to do that
better because, guess what, single moms need to be able to take
courses online, and they have to be as good as the face-to-face
delivery. So in that redesign, we are looking at all of the way
we do our work to align better to industry, and to deliver in
the best way for our students. And there's much more to do,
Congressman.
Mr. Marshall. So certainly, as an obstetrician, you're
hitting on exactly who I'm thinking of, that single mom who
maybe could get her auntie, or her sister, to come in and help
with the kids for 6 weeks or 8 weeks, but it's hard to get them
to commit to 18 weeks. One of the things that we're certainly
looking at is using Pell Grants in a non-traditional situation,
what you're describing. Hopefully we can make some progress
there at some point in time.
Dr. Ellspermann. Thank you.
Mr. Marshall. So we have the NSF person here, Dr. Lupia, as
well. What would you tell him? How could NSF work better with
community colleges and technical colleges? What ideas out there
are outside the box that you wish we could get better engaged
with NSF?
Dr. Ellspermann. I would say certainly in helping us to
adopt that technology early. We are not funded at the levels of
research institutions, as you might guess, so keeping our labs
up to date with that front-edge technology at the same time
industry's getting it, not a generation later. We really need
to have it early. Certainly Perkins helps on that front, but
that cycle of rapid change is so much quicker than it was
generations ago that we have to be able to refresh our
equipment every year, two, or three, which there's probably a
partnership to be built there.
Mr. Marshall. OK. Dr. Lupia, any return thoughts, or
comments?
Dr. Lupia. We are so grateful for the work that your
organization does, and part of our Future of Work Project is
really to try and make this information and these
collaborations happen a lot earlier. So the scientific approach
is, there's a relationship between jobs and skills. Most jobs
take a whole bunch of skills, and as jobs evolve, some of the
skills that we have now will still be relevant, but there will
be these other new skills that you can use.
So we are working with government, industry, and a whole
range of researchers to try and project, ``How are skills and
jobs likely to evolve?'' If we can figure that out, and put
that into data bases, and match it to jobs as they're evolving,
then our partners can make that data available to everyone--
because that's the idea, right? We have projects in several
States--Georgia, West Virginia now--where we're collecting data
from them, and then trying to push out real-time and usable
data about how jobs are likely to change. This can produce
really great efficiencies, because now we can tell community
colleges and others these are the skills that employers need
now, these are the skills they're likely to need 6 months from
now, 12 months from now, 24 months from now.
And with that type of data you not only get these
efficiencies, now you have this possibility someone can go to a
college and not just get the next job, but be able to be given
the skills that can help them build a career, that can take the
next two or three steps in their life. So we want to be a
tailwind to them, and very supportive.
Mr. Marshall. Sounds good. I'm going to start my Community
College Caucus here someday. I need to do that. Thank you so
much for being here, and I yield back.
Chairwoman Stevens. Great, thank you. And at this time
we're going to begin a second round of questions.
Dr. Brynjolfsson, as you've kind of defined the two urgent
economic challenges around lack of productivity growth and too
much inequality, and then gave us a list of pretty cogent and
solid recommendations on how to address those, do you mind
weighing in a little bit around some of the ethical
considerations that come up on this topic, and how those either
might be urgent right now, or might become more urgent as we
move forward?
Dr. Brynjolfsson. Absolutely. I think those are some of the
most urgent challenges. They're a little outside some of the
economics, but some of them also have an economic implication
as well. Machine learning systems have been remarkable at
helping us make all sorts of decisions, but one of the things
we've also discovered is that they're only as good as the data
that go into them, and oftentimes machine learning systems that
are trained on decisions that humans made end up perpetuating,
or even amplifying, the biases that we often have. So when it
comes to hiring, or making credit loan decisions, or who gets
parole, if the humans who are making those decisions have a set
of biases, those are going to be captured by the systems and
repeated. So there have been a number of academic studies
that--these are one of the challenges.
There's both a challenge and an opportunity there. Part of
the challenge with machine learning systems, particularly when
they're using deep neural net technology, is that it is
difficult to understand what's going on inside the black box.
They capture data, sometimes from thousands or millions of
examples, and they spew out a recommendation, and it's hard to
know exactly why, and that makes it challenging to second guess
it and say, wait a minute, this may not be right.
But the opportunity is that we can use techniques like one
called a Turing Box, where you have repeated sets of inputs,
with different characteristics going in, and sets of outputs
coming out, and you start learning what kinds of biases the
machine may have inadvertently picked up, and you can correct
those in a way that may actually, ultimately, I think, be
easier to correct than our own human biases. Because, after
all, it's not like humans are perfect either.
So I wouldn't necessarily rule out using machine learning
systems for some of these challenges, even when they are
imperfect, but we should put very high on the agenda better
understanding of some of the ethical and other biases that they
can create.
Chairwoman Stevens. And, Ms. Kowalski, coming out of your
taxonomy that you helped to lead with MXD, do you mind just
chiming in on some of the job roles that you identified that
might be pertinent to some of the points that Dr. Brynjolfsson
just talked about?
Ms. Kowalski. Yes. It's a great question. There are five
that I think really, really pop out of the work. One is what we
call the digital era enterprise ethicist, and that's a
conceptual title, of course, no one puts that out there, but it
was, you know, an individual success profile of a role of who
gets to make those decisions. Who makes the call? Who says how
far is too far?
Traditionally these decisions have been kind of bandied
about, maybe IT owns this, or Risk owns it, or Legal owns it.
Well, now, the way organizations are built in the digital era,
it does not land neatly in one of those silos, it spreads
across. And so where the buck stops actually is in a place
where no one ever imagined it. And so there are--you made a
comment earlier about how processes haven't caught up, so
that's decisionmaking processes, that's organizational
structures. It's a recognition that there's distributed
decisionmaking more and more now in organizations, and we still
have an end-of-year code-of-conduct compliance, you know, mind-
numbing 2-hours of training that we take that don't actually
get to can you identify the decisionmaking framework that your
organization uses for developing new products, solutions, or
making decisions around human beings? That's a fundamental
issue that has to be dealt with now.
A couple other things, in terms of just roles that you're
going to see popping up, obviously an organization only has one
ethicist like that, but does have to establish the framework
that supports it, but some of those specialist roles, like the
machine learning specialist, the collaborative robotic
specialist, the autonomous mobility engineer, right, how do you
make sure that, you know, people of different ethnicities are
recognized by that autonomous vehicle, right? How do you make
sure that your H.R. systems are wired not to filter people out,
but actually to bring people in, based on potential?
So those are some of those roles that we see coming up
across all organizations, and obviously a few of those are
quite specific to the manufacturing sector. And it's important
that we figure these out, because what I see right now is a lot
of organizations just trying to spread that responsibility out
without actually recognizing that those need to be defined
disciplines.
Chairwoman Stevens. As we talk about technical talent, and
the push for the hard-skilled trades, and the work that we see
out of our community colleges, and the push for people to go
into apprenticeship, and other training programs, we still feel
the need to train for analog, but also embrace the soft skill
digital. And I'm slightly over time, but with just the
remainder that I'm going to steal here, I'd love for each of
you to just comment on this shift here, and the balance of the
soft with the hardnosed technical skills that are still
required in many jobs. And, Dr. Ellspermann, if you want to
start, we'd certainly----
Dr. Ellspermann. I'd be happy to. We recognized 3 years ago
that we weren't doing enough to prepare students to be
successful in the workforce: Number one, making the right
decisions in the careers, being prepared for the world of work,
because not every student anymore comes to us already with some
prior work experience, and that they would be successful so--
building that in, so we are in the midst of rolling out what we
call our Career Coaching and Employer Connections, which
ensures every student, when they begin with us, begins building
a career action plan, which includes work and learn experiences
in industry to build some of that kind of real-world work.
We build in, certainly, soft skills throughout the
curriculum, but those skills are learned best on the job,
making sure every student has that experience before they get
out there. But it is an early and often experience, meeting
with employers being out there, interviewing, understanding
what's expected. And we know there's a lot to be done that
we've never been really asked to do in the past, but is
required by our industry, and know that that's a part of the
future.
Ms. Kowalski. So I'll pick up on this theme of moving from
analog to digital roles. So, if you were to look at the
research that we have, you'd see that 28 percent of those 165
new or highly evolved roles are sitting on the production
floor, and what we estimated was about 1 to 2 years of building
up that talent that would prepare them to take on progressively
more digital roles.
Because at the heart of it, the shift is really from doing
things physically, physical operations, to accomplishing those
operations through systems and technology. So you see a lot
more skills like quantitative, tech-assisted, optimization-
focused, integrative, mobile, virtual, and remote. That wasn't
in the lexicon, really, 5 years ago, even 3 years ago. You
know, organizations that were starting to talk about it were
the OEMs (original equipment manufacturers), for instance, that
participated in this study. Now it's spreading throughout the
supply network, and we have quite a task in front of us to gear
people up, because right now they'll have to bridge from those
more tactical analog roles into the transitional. So
organizations have to keep a foot planted firmly where they are
now, and reach for the future.
Dr. Brynjolfsson. Thank you for that question. I think this
is a very important issue, about the balance between hard and
soft skills. I teach at the Massachusetts Institute of
Technology, so certainly I have an appreciation for the
importance and value of hard skills. There are a number of
technical capabilities that our workforce is lacking and that
we need to supplement. In some cases, they can be compensated
very highly. But I also want to stress that soft skills are
increasingly the ones that are less automatable, and therefore
more humans will be needed to do those softer skills. They
often have a longer span of relevance and usefulness.
In the science article that I included as background, we
created a framework for which tasks are suitable for machine
learning. And, indeed, the ones that were less likely to be
automated were many of the softer skills, involving creativity
and interpersonal skills, persuasion, caring, coaching,
leadership, and teamwork. These are things that are very
important in the workforce, and I also think that there are
opportunities to teach them, not just on the job, but by
reinventing and reorganizing our educational curriculum. And a
research agenda to better understand the kinds of skills that
are needed going forward, I think, would be a useful supplement
to be able to map our strategies, both in education and
workforce training, going forward.
Dr. Lupia. I'd just like to state a principle and an
example. One of the overarching principles for this problem is
the idea of values-based design. So when you build a new
technology, oftentimes we're thinking about the products, and
we're not thinking about the people. And so you don't think
about the people, and the workers, and the consumers, until the
end of the process, when the unintended consequences and the
inefficiencies are already built in. A lot of our recent
misadventures with Big Tech, I think, are an example of not
thinking about the people at the beginning.
So now, when we think about the future workforce, with
values-based design, we're thinking about the people in the
workplace, and how they're going to interact. If you think
about that--starting at time one, when you start to build the
code, when you start to write the algorithms and so forth,
there are all kinds of efficiencies that you can realize later
on. And one of the efficiencies, with respect to the workforce,
is personalized practice. Because once we think about how the
new technology, and the new workplaces are going to affect
people, now we can start to understand the set of skills that
are going to be needed, and we can start to design personalized
education so that people can learn efficiently the skills that
they will need in this new place. But if you start with values
at the beginning, you get to those outcomes.
And in the point of practices, NSF is already trying to
help support this through its Advanced Technological Education
Program, or ATE. There are hundreds of community colleges and
48 ATE centers around the country that are really preparing
students for STEM (science, technology, engineering, and
mathematics) and the skilled technical workforce. We've got 17
million Americans in the skilled technical workforce now that
are in the workflow. They're building the machines, and
maintaining the computers, and so forth, and the ATE Program is
really meant to encourage and improve the training of science
and engineering technicians at both undergraduate and secondary
levels. So the things we're doing right now are things like
ATE, but the future benefit really comes from thinking through,
you know, what are the human impacts of technology?
Chairwoman Stevens. Thank you. And I'm lucky that my
colleague likes me, because I spent some of that liking capital
going slightly over, but it was really to hear from all of you,
and to have your expertise. So, at this time, I'd like to
recognize my good friend, Dr. Jim Baird, for 5 minutes of
questions.
Mr. Baird. Thank you, Madam Chair, and my question now is
going to be directed at all of you, at some point here. But,
you know, online, you know, I have grandchildren that can use
these faster than they could talk, almost, and so my question
relates to that, in a way. We're using online courses for both
formal and informal education, and so I guess the question is
this: Do we have any research that tells us what online
courses, and how to make those effective? And then also, how do
online courses, and what you're doing--and AI relate to STEM
education? We're carrying a bill about the STEM careers, and so
on. So I'm going to start with--at your left, my right, and
move that way, go ahead. Thank you.
Dr. Ellspermann. Congressman Baird, let me just say that I
think we realize that online education is here to stay. It's
not going to take over all of education. It's not the best way
for all of education. It's not the preferred learning style for
many. But we know, as I shared earlier with that single mom,
she's got to have that opportunity to learn. So we have to--as
educators, it's our responsibility to improve it constantly.
It's come through many iterations. It'll go through many more,
but it'll also be hybrid, and augmented, and many things that,
as technologies we're talking about here today, ever greater
enables us to make that online experience more real, more
virtual, more--in the way that that learner wants to learn it.
But I think we understand, as community colleges, we have
to lean in, and it's not an either/or, it's an and, it's a
both, and we need to continue to evolve. So we study, we know
we have a gap between our face-to-face and our online learning.
It's double digit right now, which is not acceptable, so our
goal is to eliminate that gap. That will be one measure of
quality, but we will continue to look for ways to make that
experience better for the online learner.
Ms. Kowalski. So I would agree with my co-panelist here
that it is a both/and. We have a number of occupations that
employers won't accept a fully virtual experience for, so they
require some sort of hands-on. I'm not going to let you touch
an aircraft wing unless you have actually touched an aircraft
wing before you come into my hangar, thankfully, right. And yet
the promise of this is pretty profound.
So if you think back to the statistics that I shared
earlier, in terms of the gap that we have facing us in
manufacturing right now, the only way to close it is to become
incredibly resourceful about who we bring in from the
sidelines. Women are certainly one untapped resource, but what
about people with physical and cognitive disabilities? Some of
the greatest advancements made in digital technologies actually
allows them to participate. The exoskeleton Dr. Lupia shared
before is a marvelous example of how we can bring people in
who, before this, have never even imagined actually having the
ability to participate in workforce.
Strictly in online education, and kind of what we think of
as the standard, this is part of how ManpowerGroup is helping
our associates upskill. We're offering all of our associates
access to free education so that they can move up, with this
idea. And just to validate what you were saying earlier, 6 to 8
weeks, that's the ability of an individual who's working full
time, sometimes two jobs, and raising kids. So it opens up more
opportunity than we've ever seen before, but it's not going to
be the only way that we can educate, because there are some
things fundamentally that require hands-on.
Dr. Brynjolfsson. Thank you for that question, Dr. Baird.
At MIT we've been doing a lot with online education for quite a
while. One of the first big courses that we did was an online
circuits design course. A couple hundred thousand people took
it. Anant Agarwal organized it. One of those students was
actually in Mongolia, and got a perfect score on it. It turned
out to be a 17-year-old boy, and it was someone who wouldn't
have been reached otherwise if there weren't this kind of
technology. MIT went ahead and admitted him to the regular
program, and it was somebody we probably wouldn't have found
otherwise.
We have put all of our regular courses online through the
Open Courseware for free. People can just access and read them.
In fact, you can see my syllabi, and see my lecture notes, and
problem sets. There's also an online system called edX. It's a
consortium of universities--it started with MITX, then Harvard
and others joined--that coordinates course materials to have
them in a little more structured way so that you go through a
curriculum. And these are what we call MOOCs, massive online
courseware. I think there was an early wave of hype and
excitement about them, you know, taking over, and doing all
sorts of things. It worked very well in some areas, like the
Circuits Course. It didn't work so well in others.
It's certainly not a silver bullet, but I think there are
four things that we've learned. One is that, for many
applications, you can get enormous scale, and much lower cost,
than we could've previously. Second, one of the unexpected
benefits was an ability to personalize. People learn at
different rates, and there's different media that work better
for other people, and you can have things extremely customized,
and even personalized, and we're learning how to do that
better. Third, it often makes sense to do a hybrid system,
where you have people meet in person, particularly for some of
the softer skills we were talking about. We often combine where
people physically get together, know their classmates, do
things together, then work separately online, then come back
together, which is actually how a lot of workforce works as
well, after all.
And then last, but not least, in fact, probably most
importantly, I think that the biggest lesson is that there is
no one best way of doing online education. What we need to do
is continually experiment and test. The success of a lot of
tech companies has been this approach of A/B testing,
constantly trying a new product, seeing if it works with
different subsets of people, and we've very much taken that to
heart with our online course offerings, and companies like
Coursera, Udacity, have been very successful in trying things.
And sometimes they work, sometimes they don't, but it's an
attitude of experiment testing. So your question was spot on,
what is the research showing what is working, and what isn't
working? And there's a whole set of things that have failed
miserably, another set of things that have succeeded. But I
think we're still in very early days, and the digital approach
allows you to gather data at a scale, and cost, and speed that
just can't be matched in other ways.
Dr. Lupia. Well, thank you for asking that question. At NSF
there's a foundation-wide effort to really support basic
research on how to develop, evaluate, and improve online
learning structures. One common way of doing it is you collect
a lot of information about the types of things people need to
know, you correlate that with information about the types of
tasks that they may be asked to do, you integrate that with
information about curricula, and how people are doing in
learning environments, and you take all that data together, and
then you can really evaluate not just what does somebody
remember after they take a test, but what can they do 6 months
later? So there's all kinds of projects like that being funded
at NSF, from trucking to farms and there's even one for
veterans. So the idea is, you know, how do you structure
curricula to help veterans who want to get into STEM pipelines,
because veterans have special abilities, and sometimes special
challenges.
I guess the biggest headline, in terms of what we've been
doing recently, is--about a year ago the Boeing company gave
$10 million to NSF to try and really boost activity in this
field. And, within the last few weeks, we have announced five
new awards to study open source learning platforms to try and
train and re-skill workers at a larger scale, and these were
just announced. It's going to be done at the University of
Southern California, Purdue, Northeastern, Colorado School of
Mines, and Oregon State University. They're all getting a
couple million dollars to test some really big ideas they have
in different ways. So it's, like--what is it, ``coopetition,''
or something? They're doing it in different ways, but they're
all going to be able to learn from each other.
And I think this is, you know, our approach is to fund a
lot of different innovations in the hope that some of them
figure out something really innovative that can be spread all
over the country.
Mr. Baird. Well, thank every one of you, and thank you,
Madam Chair, for letting us have that amount of time.
Chairwoman Stevens. Well, before we bring this hearing to a
close, it is evident that we are having a hearing with giants,
in terms of the expertise of our witnesses here today. And it
was not shared, but the new Dems have a Future of Work
Taskforce that Congressman Bill Foster chairs, and I'm a part
of. Some of our colleagues who do not sit on the House Science
Committee, we will be sharing with them this testimony here
today, all of your testimony, and the questions.
And certainly we find ourselves in a profound, and
exciting, and sometimes perplexing moment, and so your expert
testimony will guide our Committee going forward, and help us
to embrace some of these challenges, turn them into
opportunities, and continue to push forward in a measured and
data-driven way, and in a way that really respects where our
economy is heading, and can head, and how we push to continue
to support the workforce of the future.
So thank you all so much for coming to Washington today, or
taking some time to come to the Science Committee to join us
for today's hearing. This record will remain open for 2 weeks
for additional statements from Members, and for additional
questions that the Committee may ask of the witnesses, and of
which we are expecting. So, at this time, our witnesses are
excused, and this hearing is now adjourned.
[Whereupon, at 5:22 p.m., the Subcommittee was adjourned.]
Appendix I
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Answers to Post-Hearing Questions
Answers to Post-Hearing Questions
Responses by Dr. Arthur Lupia
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Appendix II
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Additional Material for the Record
Letter submitted by Representative Haley Stevens
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Executive Summary submitted by Representative Haley Stevens
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Statement submitted by Representative Haley Stevens
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