{
  "id": 7341,
  "url": "https://arxiv.org/abs/2603.11736v1",
  "title": "Gender Bias in Generative AI-assisted Recruitment Processes",
  "summary": "In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates' profiles. However, the employment of large language models (LLMs) risks reproducing, and in some cases amplifying, gender stereotypes and bias already present in the labour market. The objective of this paper is to evaluate and measure this phenomenon, analysing how a state-of-the-art generative model (GPT-5) suggest",
  "authors": "Martina Ullasci, Marco Rondina, Riccardo Coppola, Antonio Vetrò",
  "category": "research",
  "topics": "bias-fairness,jobs-economy",
  "orgs": "openai",
  "regions": null,
  "published_at": "2026-03-12T09:42:56.000Z",
  "fetched_at": "2026-07-14T16:33:08.015Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7341",
  "original_url": "https://arxiv.org/abs/2603.11736v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}