UW News

AI tools show biases in ranking job applicants’ names according to perceived race and gender

University of Washington researchers found significant racial, gender and intersectional bias in how three state-of-the-art large language models ranked resumes. The models favored white-associated names 85% of the time, female-associated names only 11% of the time, and never favored Black male-associated names over white male-associated names.

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