Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment

Abstract:AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases. A key challenge arises in unstructured CV text, where pre-trained language model embeddings may infer sensitive attributes such as gender even after explicit indicators are removed. In this paper, we evaluate nine pre-trained embedding models on the synthetic FairCVdb dataset, analyzing the informativeness of their embeddings for applicant scoring and their susceptibility to gender leakage, on both original and gender-scrubbed biographies. We further use a multi-task adversarial learning framework with gradient reversal to predict applicant suitability while suppressing gender information from learned representations. Finally, we use a multi-objective Pareto-front-based model selection to balance predictive utility and fairness. Our experimental results show that explicit gender scrubbing substantially reduces but does not eliminate gender leakage, while adversarial learning improves fairness mainly on original biographies and acts as a complementary strategy rather than a substitute for text-level debiasing.

Machine Learning (cs.LG)

arXiv:2607.20073 [cs.LG]

(or arXiv:2607.20073v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.20073 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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