When You Reject People, Tell Them Why

Why matters. To go from data to insights, you need context. Science is data plus theory, and that’s just as true when we’re assessing people as when we’re assessing ideas. As an example, algorithms rather than humans determine whether your credit score is high or low, but you should be able to find out why—and you can, because credit agencies provide a list of reasons so you can both understand and improve your score. This transparency benefits borrowers and lenders alike. By the same token, in the world of recruitment and HR, ethical employers owe job candidates and employees a similar right to explanation. To the degree that AI can reveal specific reasons for not selecting candidates, this information should be shared and may actually make the news easier to process. While AI tools aren’t free of bias, they can help mitigate it, and employers can improve decision accuracy and fairness by raising the ethical standards for designing algorithms and for sharing results.

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