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AI And Big Data Could Power A New War On Poverty


Elisabeth A Mason | NYT Syndicate

WHEN it comes to artificial intelligence 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 country. A 2013 study by the National Institutes of Health found that nearly 40 percent of medical students held a strong preference for one mode of learning: Some were listeners; others were visual learners; still others learned 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 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 Policy Making. The act creating the commission was sponsored by Senator Patty Murray, a Democrat, and Paul Ryan, the House speaker. Before the commission expired in September 2017, 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.

(Elisabeth A Mason is the founding director of the Stanford Poverty and Technology Lab and a senior adviser at the Stanford Center on Poverty and Inequality.)

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