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Stanford’s A.I. Hiring Study Exposes a Human Problem

Stanford's latest research found that a widely used A.I. hiring tool systematically reinforced biases, reinforcing a hard truth: algorithms don't create bias on their own. Organizations that want better hiring outcomes must design, govern and continuously audit A.I. systems with the same rigor they expect from human decision-makers.

How do we prevent technology designed to help us scale from scaling our biases instead? Thanks to fresh research from the Stanford Institute for Human-Centered AI, the question has become even more urgent, and the answer even more complex and uncomfortable. Researchers found that a widely used screening tool systematically rejected candidates in patterns that clearly correlated with race. 

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In theory, A.I. screening tools allow recruiters to spend less time on rote decisions and more time getting to know the people in their pipeline. In practice, however, as the Stanford study illustrates, setting and forgetting any tool designed to make decisions on a recruiter’s behalf can produce systemic biases and ultimately diminish the quality of hiring outcomes.

Getting precision at the top of the funnel requires something many A.I. hiring solutions still........

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