Back in my PhD program, I attended a conference on the role of trust in contemporary economies. This was in 2006: the subprime mortgage crisis (and subsequent Recession) hadn’t yet happened. Enron, however, was still newsworthy, with CEO Jeffrey Skilling just sentenced to a relatively brief 24 months in prison for overseeing a decade of fraud. A few years earlier, Skilling was quoted as saying his favorite book was The Selfish Gene, by the eminent biologist Richard Dawkins. His interpretation of Dawkin’s theory was juvenile, at best, but the implication was clear: in business, trust is a fool’s game.
At this conference, several economists–a profession not noted for displays of emotion–were incensed. They discussed how trust was vital when some actors have private knowledge–for example, labor markets, where potential workers knows more about their skills than future employers–and how Adam Smith’s Theory of Moral Sentiments (the other Smith classic) was in fact compatible with the invisible hand of Smith’s The Wealth of Nations. Mostly, though, they discussed the confounding fact people trust each other far more often than they should, even when betrayal is, to use the economic term, in someone’s rational self-interest.
Think about it: how many times a day do you have the opportunity to gain some small benefit by betraying a trust? You might take longer breaks than allowed, or watch videos of adorable cats instead of working. You might steal someone else’s leftover Thai food (that looks deliciously tasty). You might back into someone’s car and not leave a note. For those with Machiavellian leanings, the gains can be substantial: how many times would simply not forwarding an e-mail put a coworker (and potential rival) in a difficult situation? The risk is minimal: at worst, you can say you never received it, knowing that because most people don’t expect that level of betrayal, you won’t be found out.
The point is not that people don’t do these things–they certainly do–but that most people don’t do them most of the time. That’s why emerging technologies that promise to ensure the trustworthiness of the people you interact with–your accountant, your mechanic, your babysitter–are so fascinating. These technologies mine publicly available data to build a “trust profile” of someone you might consider bringing into your life: information on criminal convictions and lawsuits, of course, but also social media posts and forum contributions. We all know not to livestream our joyride in the company Lexus–although again, people still do–but most of us don’t consider whether our postings about Donald Trump, global warming, or the New England Patriots might influence whether someone buys our old Playstation, rents to us through Airbnb, or lets their children accept our Halloween candy.
The question of the hour is not whether people will adopt these technologies, but how they will respond when the algorithm contradicts their own intuition. Humans have evolved a pretty good system for detecting liars, unless the person lying is either very well practiced, or so deficient in empathy that he can mimic the subtle behavioral cues we unconsciously rely on. That’s why we want to meet someone face-to-face before deciding to trust them: in just a few seconds, we can decide whether the relationship is worth the risk. And we’re usually right–but not always. As the algorithms get better, they’ll be more likely to be right than we are–but not always. What then?