arXiv AI
Bias in the Tails: How Name-conditioned Evaluative Framing in Resume Summaries Destabilizes LLM-based Hiring
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arXiv AI
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Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval
arXiv:2407.20371v3 Announce Type: replace-cross Abstract: Artificial intelligence (AI) hiring tools have revolutionized resume screening, and large language models (LLMs) have the potential to do the...
arXiv Machine Learning
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Fairness Beyond Anonymization? Demographic Leakage in German LLM-Generated Resumes
The study audits demographic leakage in German-language resumes generated by large language models. Using ChatGPT, Gemini, and Qwen 3 variants, the authors generate resumes from anonymized profiles, varying only gender- and ethnicity-associated names while keeping qualifications constant. Even after anonymization and gender-neutralization, classifiers can reliably distinguish male- from female-generated resumes, driven by subtle differences in gender-neutral terminology rather than overtly gendered wording; ethnicity-related leakage remains weak.
arXiv Machine Learning
Jul 23
Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment
arXiv:2607. 20073v1 Announce Type: new Abstract: AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases.
Hugging Face Trending Papers
Jun 17
Gender Bias in LLM Hiring Decisions: Evidence from a Japanese Context and Evaluation of Mitigation Strategies
Large language models (LLMs) are increasingly deployed in hiring workflows, yet most research on gender bias in LLM hiring decisions has focused on English-language, Western-format resumes. This study examines whether pro-female gender bias extends to a Japanese corporate context and evaluates two practical mitigation strategies.
arXiv AI
Jul 14
BiasLab: A Multilingual Dual-Framing Framework for LLM Bias Measurement, Applied to Workplace and HR Contexts
arXiv:2601. 06861v2 Announce Type: replace-cross Abstract: Background: Large language models (LLMs) harbor systematic biases that are particularly consequential in workplace and HR contexts, where their outputs increasingly influence hiring, job design, and organizational decisions.
arXiv AI
Aug 3
FairFund-Bench: Evaluating Distributive Bias in LLM Resource Allocation
arXiv:2607. 28934v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly involved in the distribution of scarce resources, raising concerns about biased allocations based on characteristics like race and gender.