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

Over the past decade, there has been no shortage of examples of human bias 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 incorrectly identified him as a man stealing from security footage — a known weakness that such systems have in accurately identifying people with dark skin. In 2019, researchers showed that a software program widely used by hospitals to identify patient risks favored white people in many types of care. A few years ago, Amazon largely abandoned the system it used to screen job applicants when it discovered that it consistently preferred men over women.

How human bias is baked into AI algorithms is a complex matter.


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