lower interest rate
will pay
graduate
thank you
after-tax
hospital
You might think—or at least hope—that a polite, openly religious person who gives his word would be among the most likely to pay back a loan. But in fact this is not the case. This type of person, the data shows, is less likely than average to make good on their debt.
Here are the phrases grouped by the likelihood of paying back.
TERMS USED IN LOAN APPLICATIONS BY PEOPLE MOST LIKELY TO PAY BACK
debt-free
lower interest rate
after-tax
minimum payment
graduate
TERMS USED IN LOAN APPLICATIONS BY PEOPLE MOST LIKELY TO DEFAULT
God
promise
will pay
thank you
hospital
Before we discuss the ethical implications of this study, let’s think through, with the help of the study’s authors, what it reveals about people. What should we make of the words in the different categories?
First, let’s consider the language that suggests someone is more likely to make their loan payments. Phrases such as “lower interest rate” or “after-tax” indicate a certain level of financial sophistication on the borrower’s part, so it’s perhaps not surprising they correlate with someone more likely to pay their loan back. In addition, if he or she talks about positive achievements such as being a college “graduate” and being “debt-free,” he or she is also likely to pay their loans.
Now let’s consider language that suggests someone is unlikely to pay their loans. Generally, if someone tells you he will pay you back, he will not pay you back. The more assertive the promise, the more likely he will break it. If someone writes “I promise I will pay back, so help me God,” he is among the least likely to pay you back. Appealing to your mercy—explaining that he needs the money because he has a relative in the “hospital”—also means he is unlikely to pay you back. In fact, mentioning any family member—a husband, wife, son, daughter, mother, or father—is a sign someone will not be paying back. Another word that indicates default is “explain,” meaning if people are trying to explain why they are going to be able to pay back a loan, they likely won’t.
The authors did not have a theory for why thanking people is evidence of likely default.
In sum, according to these researchers, giving a detailed plan of how he can make his payments and mentioning commitments he has kept in the past are evidence someone will pay back a loan. Making promises and appealing to your mercy is a clear sign someone will go into default. Regardless of the reasons—or what it tells us about human nature that making promises is a sure sign someone will, in actuality, not do something—the scholars found the test was an extremely valuable piece of information in predicting default. Someone who mentions God was 2.2 times more likely to default. This was among the single highest indicators that someone would not pay back.
But the authors also believe their study raises ethical questions. While this was just an academic study, some companies do report that they utilize online data in approving loans. Is this acceptable? Do we want to live in a world in which companies use the words we write to predict whether we will pay back a loan? It is, at a minimum, creepy—and, quite possibly, scary.
A consumer looking for a loan in the near future might have to worry about not merely her financial history but also her online activity. And she may be judged on factors that seem absurd—whether she uses the phrase “Thank you” or invokes “God,” for example. Further, what about a woman who legitimately needs to help her sister in a hospital and will most certainly pay back her loan afterward? It seems awful to punish her because, on average, people claiming to need help for medical bills have often been proven to be lying. A world functioning this way starts to look awfully dystopian.
This is the ethical question: Do corporations have the right to judge our fitness for their services based on abstract but statistically predictive criteria not directly related to those services?
Leaving behind the world of finance, let’s look at the larger implications on, for example, hiring practices. Employers are increasingly scouring social media when considering job candidates. That may not raise ethical questions if they’re looking for evidence of bad-mouthing previous employers or revealing previous employers’ secrets. There may even be some justification for refusing to hire someone whose Facebook or Instagram posts suggest excessive alcohol use. But what if they find a seemingly harmless indicator that correlates with something they care about?
Researchers at Cambridge University and Microsoft gave fifty-eight thousand U.S. Facebook users a variety of tests about their personality and intelligence. They found that Facebook likes are frequently correlated with IQ, extraversion, and conscientiousness. For example, people who like Mozart, thunderstorms, and curly fries on Facebook tend to have higher IQs. People who like Harley-Davidson motorcycles, the country music group Lady Antebellum, or the page “I Love Being a Mom” tend to have lower IQs. Some of these correlations may be due to the curse of dimensionality. If you test enough things, some will randomly correlate. But some interests may legitimately correlate with IQ.
Nonetheless, it would seem unfair if a smart person who happens to like Harleys couldn’t get a job commensurate with his skills because he was, without realizing it, signaling low intelligence.
In fairness, this is not an entirely new problem. People have long been judged by factors not directly related to job performance—the firmness of their handshakes, the neatness of their dress. But a danger of the data revolution is that, as more of our life is quantified, these proxy judgments can get more esoteric yet more intrusive. Better prediction can lead to subtler and more nefarious discrimination.
Better data can also lead to another form of discrimination, what economists call price discrimination. Businesses are often trying to figure out what price they should charge for goods or services. Ideally they want to charge customers the maximum they are willing to pay. This way, they will extract the maximum possible profit.