The Filter Bubble - Eli Pariser [52]
Marketers are already exploring the gray area between what can be predicted and what predictions are fair. According to Charlie Stryker, an old hand in the behavioral targeting industry who spoke at the Social Graph Symposium, the U.S. Army has had terrific success using social-graph data to recruit for the military—after all, if six of your Facebook buddies have enlisted, it’s likely that you would consider doing so too. Drawing inferences based on what people like you or people linked to you do is pretty good business. And it’s not just the army. Banks are beginning to use social data to decide to whom to offer loans: If your friends don’t pay on time, it’s likely that you’ll be a deadbeat too. “A decision is going to be made on creditworthiness based on the creditworthiness of your friends,” Stryker said. “There are applications of this technology that can be very powerful,” another social targeting entrepreneur told the Wall Street Journal. “Who knows how far we’d take it?”
Part of what’s troubling about this world is that companies aren’t required to explain on what basis they’re making these decisions. And as a result, you can get judged without knowing it and without being able to appeal. For example, LinkedIn, the social job-hunting site, offers a career trajectory prediction site; by comparing your résumé to other peoples’ who are in your field but further along, LinkedIn can forecast where you’ll be in five years. Engineers at the company hope that soon it’ll be able to pinpoint career choices that lead to better outcomes—“mid-level IT professionals like you who attended Wharton business school made $25,000/year more than those who didn’t.” As a service to customers, it’s pretty useful. But imagine if LinkedIn provided that data to corporate clients to help them weed out people who are forecast to be losers. Because that could happen entirely without your knowledge, you’d never get the chance to argue, to prove the prediction wrong, to have the benefit of the doubt.
If it seems unfair for banks to discriminate against you because your high school buddy is bad at paying his bills or because you like something that a lot of loan defaulters also like, well, it is. And it points to a basic problem with induction, the logical method by which algorithms use data to make predictions.
Philosophers have been wrestling with this problem since long before there were computers to induce with. While you can prove the truth of a mathematical proof by arguing it out from first principles, the philosopher David Hume pointed out in 1772 that reality doesn’t work that way. As the investment cliché has it, past performance is not indicative of future results.
This raises some big questions for science, which is at its core a method for using data to predict the future. Karl Popper, one of the preeminent philosophers of science, made it his life’s mission to try to sort out the problem of induction, as it came to be known. While the optimistic thinkers of the late 1800s looked at the history of science and saw a journey toward truth, Popper preferred to focus on the wreckage along the side of the road—the abundance of failed theories and ideas that were perfectly consistent with the scientific method and yet horribly wrong. After all, the Ptolemaic universe, with the earth in the center and the sun and planets revolving around it, survived an awful lot of mathematical scrutiny and scientific observation.
Popper posed his problem in a slightly different way: Just because you’ve only ever seen white swans doesn’t mean that all swans are white. What you have to look for is the black swan, the counterexample that proves the theory wrong. “Falsifiability,” Popper argued, was the key to the search for truth: The