What jobs are affected by AI? Better-paid, better-educated workers face the most exposure - College Degreed 4 to 5 times more at risk
What jobs are affected by AI? Better-paid, better-educated workers face
the most exposure
Mark Muro, Jacob Whiton, and
Robert Maxim November 20, 2019
For media inquiries,
contact: MetroMediaRelations@brookings.edu 202.238.3139
Artificial intelligence (AI)
has generated increasing interest in “future of work” discussions in recent
years as the technology has achieved superhuman performance in a range of valuable
tasks, ranging from manufacturing to radiology to legal contracts. With that
said, though, it has been difficult to get a specific read on AI’s implications
on the labor market.
In part because the
technologies have not yet been widely adopted, previous analyses have had to
rely either on case studies or subjective assessments by experts to determine
which occupations might be susceptible to a takeover by AI algorithms. What’s
more, most research has concentrated on an undifferentiated array of “automation”
technologies including robotics, software, and AI all at once. The result has
been a lot of discussion—but not a lot of clarity—about AI, with
prognostications that range from the utopian to the apocalyptic.
Given that, the analysis
presented here demonstrates a new way to identify the kinds of tasks and
occupations likely to be affected by AI’s machine learning capabilities, rather
than automation’s robotics and software impacts on the economy. By employing a
novel technique developed by Stanford University Ph.D. candidate Michael Webb,
the new report establishes job exposure levels by analyzing the overlap between
AI-related patents and job descriptions. In this way, the following paper homes
in on the impacts of AI specifically and does it by studying empirical
statistical associations as opposed to expert forecasting.
Artificial intelligence:
What it is and how we’re measuring it
Artificial intelligence (AI)
is an increasingly powerful form of digital automation, based on machines that
can learn, reason, and act for themselves. Measuring it is hard because it is
multifarious and emergent.
AI consists of a diverse set
of technologies that serve a variety of purposes. Therefore, no single
definition can yet capture its full set of operations and capabilities.
However, broadly speaking, AI involves programming computers to do things
which—if done by humans—would be said to require “intelligence,” whether it be
planning, learning, reasoning, problem-solving, perception, or prediction.
Contrary to other forms of
automation, such as robotics and software, researchers have had little time to
learn about AI’s primary use cases in the economy.
To circumvent many of the
problems posed by AI for labor market analysis then, this brief leverages a
novel method created by Stanford Ph.D. candidate Michael Webb to quantify the
exposure of occupations to AI, in order to assess the broader labor market
impacts. (See Michael Webb, “The Impact of Artificial Intelligence on the Labor
Market.”)
Along these lines, the present
analysis uses machine learning in the form of natural language processing to
quantify the overlap between text from patents filed for AI technologies, and
job descriptions from the U.S. Department of Labor’s O*NET database.
This process allowed Webb to
generate a measure of every occupation’s varying levels of “exposure to AI
applications in the near future.” These scores were then normalized to aid in
comparing them with one another. As a result, “exposure” scores in this paper
do not indicate the percentage of tasks that can be replaced by AI, but rather
indicate each job’s relative exposure above (positive numbers) or below
(negative numbers) the average job’s exposure to AI.
Read more about what AI is
and how we’re measuring it on page 5 of the full report. »
Findings
In contrast to past
analyses, this report finds that better paid professionals and bigger,
high-tech metro areas are the most exposed to AI.
White-collar jobs
(better-paid professionals with bachelor’s degrees) along with production
workers may be most susceptible to AI’s spread into the economy
AI could affect work in
virtually every occupational group. However, whereas research on automation’s
robotics and software continues to show that less-educated, lower-wage workers
may be most exposed to displacement, the present analysis suggests that
better-educated, better-paid workers (along with manufacturing and production
workers) will be the most affected by the new AI technologies, with some
exceptions.
Our analysis shows that
workers with graduate or professional degrees will be almost four times as
exposed to AI as workers with just a high school degree. Holders of bachelor’s
degrees will be the most exposed by education level, more than five times as
exposed to AI than workers with just a high school degree.
Read more about the
occupations exposed to AI on page 11 of the full report. »
Our analysis shows that AI
will be a significant factor in the future work lives of relatively well-paid
managers, supervisors, and analysts. Also exposed are factory workers, who are
increasingly well-educated in many occupations as well as heavily involved with
AI on the shop floor. AI may be much less of a factor in the work of most
lower-paid service workers.
Source: Brookings analysis
of Webb (2019)
Men, prime-age workers, and
white and Asian American workers may be the most affected by AI
Men, who are overrepresented
in both analytic-technical and professional roles (as well as production), work
in occupations with much higher AI exposure scores. Meanwhile, women’s heavy
involvement in “interpersonal” education, health care support, and personal
care services appears to shelter them. This both tracks with and accentuates
the finding from our earlier automation analysis.
Read more about AI’s
exposure levels to different demographics on page 16 of the full report. »
Bigger, higher-tech metro
areas and communities heavily involved in manufacturing are likely to
experience the most AI-related disruption
While AI will be employed
virtually everywhere, its inroads will vary across space, determined by the
local industry, education, and occupational mix. Contrary to the automation
susceptibility maps, the present AI analysis reveals that smaller, more rural
communities are significantly less exposed to technological disruption than
larger, denser urban ones. This likely reflects the basic urban geography of
the information, technology, and professional-managerial economy, with its
orientation toward analytics, prediction, and strategy—all susceptible to AI.
With that said, multiple metros and rural areas in the Heartland will likely
contend with widespread AI given their orientation to agricultural, production,
extraction, and transportation work.
Read more about which
communities will be most exposed to AI on page 18 of the full report. »
Source: Brookings analysis
of Webb (2019)cts which places and sectors will be impacted by AI, how these
impacts play out is still an open question.
In conclusion, past
“automation” analyses—including our own—have likely obscured AI’s distinctive
impact. Yet here too much is an open question. Most notably, this brief
quantifies only the potential exposure of occupations to AI—not whether
adoption has occurred or how it will affect the completion of work.
In this regard, while the
present assessment predicts areas of work in which some kind of impact is
expected, it doesn’t specifically predict whether AI will substitute for
existing work, complement it, or create entirely new work for humans.
That means much more
inquiry—qualitative and empirical—is needed to tease out AI’s special genius
and coming impacts.
References
Webb, Michael. 2019. “The
Impact of Artificial Intelligence on the Labor Market.” Stanford University
Working Paper.
Thanks to Alec Friedhoff for
interactive data visualization.
https://www.brookings.edu/research/what-jobs-are-affected-by-ai-better-paid-better-educated-workers-face-the-most-exposure/?te=1&nl=bits&emc=edit_tu_20191122?campaign_id=26&instance_id=14021&segment_id=19015&user_id=97aa00b6b5ebcf36dfdfb16210fa9843®i_id=8359153220191122
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