AI evaluations of job candidates look unbiased without being fair

Researchers tested large language models on startup pitches and found that the systems look unbiased without being fair. The tools gave higher average scores to underrepresented groups, yet these candidates did not win more often. Reviewers and policymakers must look past surface scores to check for hidden bias.

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