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A practical approach for addressing bias in artificial intelligence – Press and Guide

Over the past decade, there’s been no shortage of examples of human biases creeping into artificial intelligence processes.

Back in 2020, Robert Williams, a Black Farmington Hills resident, was arrested and jailed after a police facial recognition algorithm wrongfully identified him as a man shoplifting on security footage — a known weakness such systems have in accurately identifying people with darker skin. In 2019, researchers demonstrated that a software system widely used by hospitals to identify patient risks was preferencing white people for many types of care. A few years ago, Amazon mostly abandoned a system it was using to screen job applicants when it discovered it was consistently favoring men over women.

How human biases get baked into AI algorithms is a complicated phenomenon.

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