Traditional bias detection methods often assess gender bias in isolation, overlooking how gender interacts with race, age, or disability. Intersectionality encourages more comprehensive bias audits, prompting researchers to evaluate AI performance across multiple intersecting groups to better identify and mitigate nuanced bias patterns.
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