Transformer models detect applicant gender in de-gendered academic recommendation letters via implicit linguistic patterns such as associations with words like 'emotional' and 'humanitarian', and removing these cues reduces but does not eliminate prediction accuracy above chance.
Powerless men and agentic women: Gender bias in hiring decisions.Sex Roles, 80(11):667–680
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Identifying and Mitigating Gender Cues in Academic Recommendation Letters: An Interpretability Case Study
Transformer models detect applicant gender in de-gendered academic recommendation letters via implicit linguistic patterns such as associations with words like 'emotional' and 'humanitarian', and removing these cues reduces but does not eliminate prediction accuracy above chance.