In the early 2000s, Alex Pentland was running the wearable computing group at the MIT Media Lab—the place where the ideas behind augmented reality and Fitbit-style fitness trackers got their start. Back then, it was still mostly folks wearing computers in satchels and cameras on their heads. “They were basically cell phones, except we had to solder it together ourselves,” Pentland says. But the hardware wasn't the important part. The ways the devices interacted was. “You scale that up and you realize, holy crap, we’ll be able to see everybody on Earth all the time,” he says—where they went, who they knew, what they bought.

And so by the middle of the decade, when massive social networks like Facebook were taking off, Pentland and his fellow social scientists were beginning to look at network and cell phone data to see how epidemics spread, how friends relate to each other, and how political alliances form. “We’d accidentally invented a particle accelerator for understanding human behavior,” says David Lazer, a data-oriented political scientist then at Harvard. “It became apparent to me that everything was changing in terms of understanding human behavior.” In late 2007 Lazer put together a conference entitled “Computational Social Science,” along with Pentland and other leaders in analyzing what people today call big data.

In early 2009 the attendees of that conference published a statement of principles in the prestigious journal Science. In light of the role of social scientists in the Facebook-Cambridge Analytica debacle—slurping up data on online behavior from millions of users, figuring out the personalities and predilections of those users, and nominally using that knowledge to influence elections—that article turns out to be prescient.

“These vast, emerging data sets on how people interact surely offer qualitatively new perspectives on collective human behavior,” the researchers wrote. But, they added, this emerging understanding came with risks. "Perhaps the thorniest challenges exist on the data side, with respect to access and privacy,” the paper said. “Because a single dramatic incident involving a breach of privacy could produce rules and statutes that stifle the nascent field of computational social science, a self-regulatory regime of procedures, technologies, and rules is needed that reduces this risk but preserves research potential.”

Oh. You don’t say?

Possibly even more disturbing than the idea that Cambridge Analytica tried to steal an election—something lots of people say probably isn’t possible—is the role of scientists in facilitating the ethical breakdowns behind it. When Zeynep Tufekci argues that what Facebook does with people’s personal data is so pervasive and arcane that people can’t possibly give informed consent to it, she’s employing the language of science and medicine. Scientists are supposed to have acquired, through painful experience, the knowledge of how to treat human subjects in their research. Because it can go terribly wrong.

Here’s what’s worse: The scientists warned us about big data and corporate surveillance. They tried to warn themselves.

In big data and computation, the social sciences saw a chance to grow up. “Most of the things we think we know about humanity are based on pitifully little data, and as a consequence they’re not strong science,” says Pentland, an author of the 2009 paper. “It’s all stories and heuristics.” But data and computational social science promised to change that. It’s what science always hopes for—not merely to quantify the now but to calculate what’s to come. Scientists can do it for stars and DNA and electrons; people have been more elusive.