Fine-Tuning Strategy, Not Just Data: A Statistically Rigorous Audit of Gender and Stereotypical Bias in Embedding Models for HR Recommendation Systems

Abstract:Embedding models underpin modern job-candidate matching: resumes, job descriptions, and queries are encoded as dense vectors, and recommendations come from nearest-neighbor retrieval over that space. Any social bias in the embeddings therefore feeds directly into HR recommendations. We audit six multilingual sentence-embedding models (LaBSE- and E5-family, both public and custom fine-tuned) using Bias-in-Bios (occupational gender bias) and StereoSet (stereotypes across profession, race, gender, and religion). We treat statistical validity as a first-class concern: we identify a non-independence error in a naive significance test, where pooling non-independent pairwise similarities inflated a trivial 0.0046 mean difference to $p \approx 0$, and replace it with a profession-level paired Wilcoxon signed-rank test, individual-resampling bootstrap confidence intervals, and permutation tests for StereoSet. Under the corrected methodology, knowledge-distillation (KD) fine-tuning of E5-Base significantly increases measured bias on Bias-in-Bios ($\Delta = -0.177$, $p < 10^{-8}$; more negative means more bias), although this measure is sensitive to global embedding geometry. The KD result is corroborated by pro-stereotype patterns in the StereoSet profession category (uncorrected $p < 0.05$). Binary-classification fine-tuning of the same base model shows no statistically distinguishable change ($p = 0.218$). Fine-tuning strategy is thus an actionable design lever for practitioners building HR recommenders. We also report which benchmark categories are underpowered, and note that two domain-adapted models could not be included in significance testing due to a software-access constraint, which limits our conclusions.

8 pages, 4 figures, 5 tables. Accepted at RecSys in HR '26 (The 6th Workshop on Recommender Systems for Human Resources)

Information Retrieval (cs.IR); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)

H.3.3; I.2.7; K.4.2

arXiv:2610.14579 [cs.IR]

(or arXiv:2610.14579v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2610.14579 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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