arXiv AI
Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval
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arXiv AI
Jul 29
Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
arXiv:2507. 11548v3 Announce Type: replace-cross Abstract: The use of publicly available generative AI systems for resume evaluation is often justified by the assumption that these tools reduce bias relative to human judgment.
arXiv AI
Jun 3
Generating the Modal Worker: A Cross-Model Audit of Race and Gender in LLM-Generated Personas Across 41 Occupations
arXiv:2510. 21011v3 Announce Type: replace-cross Abstract: As generative AI tools are increasingly used to portray people in professional roles, understanding their racial and gender representational biases is critical.
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
Aug 24
Who Do Language Models Think Is Competent? A Mechanistic Analysis of Occupational Bias
The paper investigates whether language models still encode occupational biases even when they appear unbiased in behavioral tests. Using a causal framework, the authors separate bias into internal representations of user competence and observable outputs, deriving steering vectors that show these representations influence model behavior in question‑answering and hiring tasks. Across several open‑weight models, demographic factors such as gender, race, and socioeconomic status affect the models’ internal competence representations, revealing hidden bias that behavioral metrics alone may miss.
arXiv AI
2d ago
General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level
arXiv:2311.13892v4 Announce Type: replace-cross Abstract: The social biases and unwelcome stereotypes revealed by pretrained language models are becoming obstacles to their application. Compared to n...