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

Source

Source name: arXiv
Source kind: paper
Authors / organization: Farnaz Faramarzi Lighvan, Lynn Houthuys
Venue: arXiv: cs.LG
Published: 2026-07-22
Collected: 2026-07-23
URL: http://arxiv.org/abs/2607.20073v1
PDF: https://arxiv.org/pdf/2607.20073v1
DOI: UNKNOWN

Plain-Language Brief

Researchers evaluate the bias in pre-trained language models used for AI-based recruitment systems, focusing on gender leakage from CV data. They analyze nine models using a synthetic dataset and a multi-task adversarial learning framework to improve fairness.

What the Document Says

The research discusses the issue of social biases in AI-based recruitment systems that use machine learning models trained on historical CV data.
A challenge is identified with unstructured CV text, where pre-trained language model embeddings may reveal sensitive attributes such as gender even when explicit indicators are removed.
Nine pre-trained embedding models are evaluated using a synthetic dataset called FairCVdb to analyze their usefulness for applicant scoring and susceptibility to gender leakage on both original and gender-scrubbed biographies.
A multi-task adversarial learning framework with gradient reversal is employed to predict applicant suitability while suppressing gender information from learned representations.
A multi-objective Pareto-front-based model selection approach is used to balance predictive utility and fairness.

Key Concepts Mentioned

AI-based recruitment systems
social biases
pre-trained language models
gender leakage
embeddings
FairCVdb dataset
multi-task adversarial learning

Claims Made by the Document

Pre-trained language model embeddings may reveal sensitive attributes such as gender even when explicit indicators are removed.
Explicit gender scrubbing can reduce but not eliminate gender leakage.

Evidence Used

The text states that the researchers evaluate nine pre-trained embedding models to analyze their susceptibility to gender leakage.
The text mentions the use of a synthetic dataset (FairCVdb) and a multi-task adversarial learning framework.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.