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. A.I. can improve this state of affairs. Even within the context of a standardized curriculum, 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. In this way, the A.I. “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 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. 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 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.
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 authorized 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 A.I. 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.