Artificial intelligence could take important decisions out of the hands of biased people and flawed processes and entrust them to supposedly objective algorithms. The dream is of prison sentences untinged by racism, and hospital care that prioritizes the neediest patients with pinpoint accuracy.
But the algorithms that power these platforms are only as good as the data supplied to them and the humans who build them. Sendhil Mullainathan, the Roman Family University Professor of Computation and Behavioral Science, finds evidence that a faulty algorithm unfairly excluded Black patients from hospital care-management programs. Even when algorithms have good data, Jean-Pierre Dubé, the Sigmund E. Edelstone Professor of Marketing and a Charles E. Merrill Faculty Scholar, sees ways in which they could be unintentionally racist.
However, Mullainathan’s research also points to the potential for enormous gains in both efficiency and equity using the transformational power of AI. With the right regulatory regime in place, he argues, algorithms can be scrutinized for bias without sacrificing their potential benefits.
“The Achilles’ heel of all algorithms is the humans who build them and the choices they make about outcomes, candidate predictors for the algorithm to consider, and the training sample. A critical element of regulating algorithms is regulating humans.”
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