Algorithms may be biased, but people certainly are. 

Financial-services companies are using non-traditional data cues to separate signals from noises in determining who should receive loans. I’d think in the short term such code, if written well, may be fairer. It certainly has the potential, though, to drift in the wrong direction over time. If our economic well-being is based on real-time judgements of our every step, then we could begin mimicking behaviors that corporations desire, and, no, corporations still aren’t people.

From Quentin Hardy at the New York Times:

Douglas Merrill, the founder and chief executive of ZestFinance, is a former Google executive whose company writes loans to subprime borrowers through nonstandard data signals.

One signal is whether someone has ever given up a prepaid wireless phone number. Where housing is often uncertain, those numbers are a more reliable way to find you than addresses; giving one up may indicate you are willing (or have been forced) to disappear from family or potential employers. That is a bad sign. …

Mr. Merrill, who also has a Ph.D. in psychology…thinks that data-driven analysis of personality is ultimately fairer than standard measures.

“We’re always judging people in all sorts of ways, but without data we do it with a selection bias,” he said. “We base it on stuff we know about people, but that usually means favoring people who are most like ourselves.” Familiarity is a crude form of risk management, since we know what to expect. But that doesn’t make it fair.

Character (though it is usually called something more neutral-sounding) is now judged by many other algorithms. Workday, a company offering cloud-based personnel software, has released a product that looks at 45 employee performance factors, including how long a person has held a position and how well the person has done. It predicts whether a person is likely to quit and suggests appropriate things, like a new job or a transfer, that could make this kind of person stay.•