A.I., Big Data could power new war on poverty
We ought to consider their potential to work to society’s advantage
When it comes to artificial intelligence and jobs, the prognostications are grim. The conventional wisdom is that A.I. 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 A.I.’s potential benefits for society as well as its drawbacks. The big-data and A.I. 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). A.I. can address all three.
First, even as A.I. threatens to put people out of work, it can simultaneously be used to match them to good middle-class jobs that are going unfilled. This is precisely the kind of matching problem at which A.I. excels. Likewise, A.I. 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. 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 per cent 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. A.I. can improve this state of affairs. A.I. “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 A.I., 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.
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 programs 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 programs help certain people at a given time and to quickly assess whether programs are having the desired effect. 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 programs. 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.