Up to now, the focus on the power and implications of Big Data technology has been involved social media, business decision-making and online privacy. Those are big subjects in their own right. So it’s not surprising that the notion of a data-driven society has not been much considered.
But someone who has was host of the meeting: Alex Pentland, a computational social scientist at the Media Lab. He put his intellectual stake in the ground last year in a presentation posted on Edge.org, “Reinventing Society in the Wake of Big Data.”
Mr. Pentland’s starting point is that the most important data that is becoming available on a vast new scale is information about people’s behavior. For example, he cites location data from cellphones and evermore consumption data as people increasingly use credit cards for even the smallest purchases. He distinguishes this behavioral data from less-telling data — about people’s beliefs like Facebook communications or Google searches.
The fine-grained behavioral data, according to Mr. Pentland, opens the way to changing how we think about society and how a society is governed. Adam Smith and Karl Marx, he explains, thought about markets and classes, respectively. “But those are aggregates,” he said. “They’re averages.”
Yet now, Mr. Pentland says, it becomes possible to track social phenomena down to the individual level and the social and economic connections among individuals. The ability to monitor these “micro-patterns,” Mr. Pentland said, means “we’re entering a new era of social physics.”
What might that mean in practice? Reed Hundt, the chairman of the Federal Communications Commission in the Clinton administration, observed at the meeting that Big Data played a major role in the last election — a reference to the Obama campaign’s deft use of data analysis to identify potential Obama voters and encourage them to cast their ballots.
“You get elected with Big Data, but you govern without it,” Mr. Hundt said. “How much sense does that make?”
Mr. Hundt, chief executive for the Coalition for Green Capital, a nonprofit organization, pointed to the waste in a range of government incentive and benefit programs, from tax credits for solar panels to Social Security, that results from the across-the-board approach — or policy by averages, as Mr. Pentland might put it.
Instead, a data-driven approach to solar-energy incentives would concentrate government incentives to where the payoff is greatest in terms of efficiently generating alternative energy — larger buildings with a lot of roof space instead of small houses, Mr. Hundt said. A by-the-data model for benefits programs, he added, would suggest means-testing Social Security payments as well as adjusting payments locally for differences in costs of living.
“So all men are created equal, but are subsidized individually,” Mr. Hundt quipped.
Intriguing, and perhaps wise policy, but it would also seem to be a redefinition of fairness as it applies to broad benefit programs, like Social Security and Medicare, which typically make standard payments and avoid means testing.
What are the chances such a data-driven course would be politically acceptable? If the data points the way to greater efficiency, why not, Mr. Hundt replied. After all, he said, a major role of government is to transfer income to people who would benefit most — better data, closely analyzed, means government can perform that role more effectively, Mr. Hundt said.
An underlying assumption of tilting toward a data-driven society is that, as one participant put it, “information over time wins out.” That is, data will change attitudes and policy, combating bias and causing policy-making to be more of a science. To data optimists, then, the endless political squabbling and stalemate in Washington points to all the room there is for improvement.
In a Big Data world, the data-mining for patterns and insights to guide policy will be done automatically — by software algorithms. Of course, algorithms are created by people and they contain inferences and assumptions coded in. Those coded-in values shape the output — computer-generated predictions, recommendations and simulations.
That raises question of the human design and control of the computerized helpers in policy-making, as in other realms of decision-making. “At some point, you’re in the hands of the algorithm,” observed John Henry Clippinger, chief executive of the Institute for Data Driven Design, a nonprofit research and educational organization. “You’re whistling in the dark if you don’t think that day is coming.”