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This paper audits 14 large language models for hiring discrimination using a paired-resume methodology, finding that older models exhibit pro-White bias while newer models show null or pro-Black bias, indicating a reversal in algorithmic hiring bias across model generations.
Stanford HAI reports that AI hiring tools can yield racial bias and systemic rejection due to algorithmic monocultures, where similar models lead to widespread discrimination.