{
  "id": 956,
  "url": "https://arxiv.org/abs/2606.16723v1",
  "title": "AgentFairBench: Do LLM Agents Discriminate When They Act?",
  "summary": "Large language model (LLM) agents increasingly take actions (screening applicants, recommending credit, triaging patients), yet fairness for LLMs is still measured by grading answers. We introduce AgentFairBench, a cheap, reproducible, multi-domain benchmark for demographic disparity in the actions of LLM agents. Grounded in a companion framework, the Bias Conduction Framework (BCF, restated here), it spans three regulator-anchored domains: hiring, lending, and medical triage. Synthetic, demogra",
  "authors": "Triveni Morla, Rohith Reddy Bellibaltu, Manpreet Singh, Manmeet Singh Kapoor",
  "category": "research",
  "topics": "bias-fairness,regulation,healthcare,agents-autonomy",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-15T13:50:26.000Z",
  "fetched_at": "2026-07-14T14:14:54.534Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/956",
  "original_url": "https://arxiv.org/abs/2606.16723v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}