White House 2019 Economic Report Puts Shine On AI And Its Meaning For Future Of Work – Analysis – Eurasia Review

Policy makers are asked to worry deeply about how workers will deal with the costs of reskilling if they aren’t paid for the duration of the reskilling period or their wages aren’t enough to pay the tuition.

By Nikhila Natarajan

The White House has put a brand new shine on its artificial intelligence (AI) game devoting 42 pages of its latest annual economic report [1] to AI in practice and the way machine learning systems interact with humans, social structures and institutions.

During
the same week, the White House sent another signal that it’s serious
about AI research and launched a new internet portal AI.gov that pulls together the administration’s policies on AI on a single landing page.

While
the economic report dusts out the theme song of the present milieu —
anxiety about the future of work — it concerns itself less with a wild
west scenario where robots run amok and more with the transitions
already unravelling in the workplace.

Policy
makers are asked to worry deeply about how workers will deal with the
costs of reskilling if they aren’t paid for the duration of the
reskilling period or their wages aren’t enough to pay the tuition. The
report is an attempt to elevate the way political circles engage with
the prospect of automation affecting employment.

Chapter
7 of the 533 page report tells us that sporting a completely non-techie
label just doesn’t cut it anymore, workers without a college degree are
most at risk for displacement by automation and workers in jobs that
tend to require more routine and manual skills are under intense
pressure to upskill for IT-based tasks. “Individuals in occupations that
involve greater IT-based tasks are continually experiencing rising wage
premiums. All these pieces of empirical evidence point to the need for
digital skills in the emerging labor market.” The opportunity for
reskilling is perhaps greatest in the field of cybersecurity, the report
tells us.

The report dwells longer than
in years past on the anxieties surrounding the word AI and its many
meanings in the world of work. Drawing from a wealth of old and new
research and bestselling books on the subject, the report brings good
tidings for workers in the crosshairs of technological change: a
“narrow, static focus on possible job losses paints a misleading picture
of AI’s likely effects on the country’s economic well-being.”

CEA
Chairman Kevin Hassett sounded upbeat in his on-the-record comments
during a White House conference call: “I think that the headline from
the AI chapter, from the AI part of that chapter — is really that there
are a lot of different scenarios. But, one that we expect will be very
important, is going to be very similar to the Uber scenario. Where the
Uber software was made, so that taxi dispatchers were no longer needed
and that cars themselves are much more efficient. And, so you might have
expected that job employment would go down in this sector because there
would be no dispatchers and the cars are more efficient, so that there
would be fewer drivers. But the elasticity of demand for Uber rides and
Lyft and, of course, other similar apps was so high that the employment,
as we document in the chapter, actually skyrocketed in that space
without people seeing lower wages. And so we think that the technology
area holds a great deal of promise going forward.”

The CEA document sets up the discussion of AI around these broad themes:

  • How AI affects the value of skill sets and at what rate they depreciate.
  • More specifically, what are the skills workers need to layer onto the typical jobs they do.
  • Economic vulnerabilities brought on by diffusion of technology and mobile computing.
  • The enormous skills gap and the resulting opportunity to upskill for cybersecurity jobs.
  • Policy challenges lawmakers will likely face in the years ahead because of all the above.

Of
these, reskilling for cybersecurity jobs has been getting a lot of
spotlight. There are an estimated 300,000 cybersecurity job vacancies in
the US and this is where opportunity for reskilling is greatest, says
the report. Here are some of the top takeaways:

Reskilling? Consider a cybersecurity job

Across
the lion’s share of 50 American states, half of a state’s existing
cybersecurity workforce would need to change jobs every year to meet new
postings. That speaks to the turnover needed to meet the current skills
gap. Recent estimates from International Information System Security
Certification Consortium ISC2 points to a “shortage of 2.9 million
cybersecurity employees globally.” Talent in this domain can take years
to cultivate; coders, developers, malware analysts are all in high
demand. US President Donald Trump recently signed an executive order aimed at boosting the country’s cybersecurity workforce. The order
requires the government to provide better access to cybersecurity skills
training, to identify the most skilled workers and to help advance
careers.

Majority of Fortune 500 companies ‘fail to take most basic cyber security measures’

Rapid7,
an Internet security firm which collects public data on cybersecurity
practices of any firm with an online presence, shared its 2018 data for
Fortune 500 companies with the CEA. Data from here shows that the
majority of Fortune 500 companies are vulnerable to cyber attacks, and
don’t have “even the most basic security measures” in place.

As
the report says, this is likely because chief information officers lack
the authority to make the organisation-wide decisions and are yet
charged with maintaining network security.

A turbocharged demand curve for price-elastic goods

The
report turns the mirror to the Uber and Lyft model to say that lower
costs from better AI and ML will set off a turbocharged demand curve for
price elastic products. This is in sharp contrast to how demand scales
for price inelastic products like say in agriculture.

AI is helping teachers improve education outcomes

Georgia
State University, the report says, has seen a 3.3 percentage-point
increase in the probability that students will enroll on time based on
the effects of customised text messages during the college enrollment
process. The report cites significant returns on student outcomes from
Edtech programs like the one in Arizona State University which uses
adaptive learning platforms that help teachers offer more targeted
learning experiences based on real-time intelligence on how well their
students understand each concept. “Given that at least 54 percent of all
employees will require significant reskilling and/or upskilling by 2022
(World Economic Forum 2018), educational institutions will need to
become increasingly adaptive, finding ways to integrate technology to
simultaneously reduce costs, improve quality, and raise agility.”

AI and labour productivity

If
AI is all it’s cracked up to be, then why labour productivity isn’t
buzzing? Have the productivity effects of technology been oversold?
“Perhaps the strongest argument for why productivity statistics in
recent history have not shown the expected benefits from the new
technologies is that, for practical reasons, there have so far simply
been lags between productivity and the widespread implementation of AI
and ML,” the report reads. Drawing from the research of Paul David,
Brynjolfsson, Rock, and Syverson, the CEA report suggests we are simply
awaiting the results of a necessary trial-and-error process.
Productivity benefits will flow when the fruits from complementary
investments begin to have their full impact. There’s another side to the
complementarity principle: “Skill-biased technical change” can account
for most of the earnings disparities between higher-skilled workers
whose productivity increases with more automation versus lower-skilled
workers — a trend “amplified during the IT revolution.”

When is AI an input? When is it strategic? What does that mean for us?

Consider
this excerpt: “The principle of comparative advantage tells us that
human workers can benefit from being in the same market with machines,
even if these machines excel at many traditionally human tasks. The
benefit comes from workers specialisation in the tasks which humans can
do better than machines, or at least the tasks where humans are at the
smallest disadvantage (Autor 2015). Specialisation allows the machines
to be used on their best tasks without wasting resources on tasks that
people can do at a lower opportunity cost. To put it in another way,
even if it were technologically possible to let machines do all tasks,
and do them better than humans do, an owner of the machines would
sacrifice profits by deploying them without regard for specialisation.”

The
anatomy of decision making is central to how the report approaches the
value of human judgement and skill in the age of automation. Goldfarb,
Gans and Agrawal unpack this in great detail in their their book
Prediction Machines: “Prediction machines increase the returns to
judgment because, by lowering the cost of prediction, they increase the
value of understanding the rewards associated with actions. However,
judgment is costly. Figuring out the relative payoffs for different
actions in different situations takes time, effort, and
experimentation.”

Leading AI scholars
have welcomed the overall status upgrade the White House has given to AI
research in recent weeks. Gans and Goldfarb, in an email reply to ORF,
welcome the immersive content for its “full chapter treatment and
investigation in the main economic report from the US government.”


[1]The Economic Report of the President, 2019.

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