AI and big data
could power a new war on poverty
When it
comes to artificial intelligence (AI) and jobs, the prognostications are grim.
The conventional wisdom is that AI might soon put millions of people out of
work - that it stands poised to do to clerical and white-collar workers over
the next two decades what mechanisation did to factory workers over the past
two. And that is to say nothing of the truckers and taxi drivers who will find
themselves unemployed or underemployed as self-driving cars take over our
roads.
But it's
time we start thinking about AI's potential benefits for society as well as its
drawbacks. The big data and AI revolutions could also help fight poverty and
promote economic stability.
Poverty, of
course, is a multifaceted phenomenon. But the condition of poverty often
entails one or more of these realities: a lack of income (joblessness); a lack
of preparedness (education); and a
dependency on government services (welfare).
AI can address all three.
First, even as AI threatens to put people out of work, it can simultaneously be
used to match them to good middle-class jobs that are going unfilled. Today
there are millions of such jobs in the United States. This is precisely the
kind of matching problem at which AI excels. Likewise, AI can predict where the
job openings of tomorrow will lie, and which skills and training will be needed
for them.
Historically
we have tended to shy away from this kind of social planning and job matching,
perhaps because it smacks to us of a command economy. No one, however, is
suggesting that the government should force workers to train for and accept particular
jobs - or indeed that identifying these jobs and skills gaps needs to be the
work of the government. The point is that we now have the tools to take the
guesswork out of which jobs are available and which skills workers need to fill
them.
Second, we
can bring what is known as differentiated education - based on the idea that
students master skills in different ways and at different speeds - to every
student in the US. A 2013 study by the National Institutes of Health found that
nearly 40 per cent of medical students held a strong preference for one mode of
learning: Some were listeners; others were visual learners; still others learnt
best by doing.
Our school
system effectively assumes precisely the opposite. We bundle students into a
room, use the same method of instruction and hope for the best. AI can improve
this state of affairs. Even within the context of a standardised curriculum, AI
"tutors" can home in on and correct for each student's weaknesses,
adapt coursework to his or her learning style and keep the student engaged.
Today's
dominant type of AI, also known as machine learning, permits computer programs
to become more accurate - to learn, if you will - as they absorb data and
correlate it with known examples from other data sets. In this way, the AI
"tutor" becomes increasingly effective at matching a student's needs
as it spends more time seeing what works to improve performance.
Third, a
concerted effort to drag education and job training and matching into the 21st
century ought to remove the reliance of a substantial portion of the population
on government programmes designed to assist struggling Americans. With
21st-century technology, we could plausibly reduce the use of government
assistance services to levels where they serve the function for which they were
originally intended.
Big data
sets can now be harnessed to better predict which programmes help certain
people at a given time and to quickly assess whether programmes are having the
desired effect. To use an advertising analogy, this would be the difference
between placing a commercial on prime-time television and doing so through
micro-targeted analytics. Guess which one is cheaper and better able to reach
the target population?
As for the
poisonous effect of ideology on the debate over public assistance: Big data
promises something closer to an unbiased, ideology-free evaluation of the
effectiveness of these social programmes. We could come closer to the vision of
a meritocratic, technocratic society that politicians from both parties at
state and local levels - those closest to the practical problems their
constituents face - have begun to embrace.
Even the US
Congress occasionally wakes up from its partisan slumber to advance the cause
of technology in public policy decision-making: In 2016, Congress voted for and
President Barack Obama authorised the creation of the Commission on
Evidence-Based Policymaking.
The Act
creating the commission was sponsored by Senator Patty Murray, a Democrat, and
House Speaker Paul Ryan. Before the commission expired last September, it used
government data to evaluate the effectiveness of government policy and made
recommendations based on its findings.
This
provides one more indication of the promise of AI and big data in the service
of positive, purposeful public good. Before we dismiss these new technologies
as nothing more than agents of chaos and disruption, we ought to consider their
potential to work to society's advantage.
Regards
Pralhad Jadhav
Senior Manager @ Knowledge
Repository
Khaitan & Co
Twitter Handle | @Pralhad161978
No comments:
Post a Comment