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.
Disability, fairness, and algorithmic bias in ai recruitment.Ethics and Inf
2 Pith papers cite this work. Polarity classification is still indexing.
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Supply chain dependencies in AI hiring systems create bias from untestable component interactions and unassignable accountability due to proprietary information asymmetries.
citing papers explorer
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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.
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How Supply Chain Dependencies Complicate Bias Measurement and Accountability Attribution in AI Hiring Applications
Supply chain dependencies in AI hiring systems create bias from untestable component interactions and unassignable accountability due to proprietary information asymmetries.