{
  "id": 15686,
  "url": "https://arxiv.org/abs/2607.28934v1",
  "title": "FairFund-Bench: Evaluating Distributive Bias in LLM Resource Allocation",
  "summary": "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. Recent LLM audits have produced inconsistent results, however, finding evidence of both positive and negative discrimination towards women and ethnic minorities, even for the same models. We show that this disagreement can arise from differences in audit format and introduce FairFund-Bench, a benchmark that system",
  "authors": "Martin Lukk",
  "category": "research",
  "topics": "bias-fairness,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-31T01:29:16.000Z",
  "fetched_at": "2026-08-03T05:10:47.622Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/15686",
  "original_url": "https://arxiv.org/abs/2607.28934v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}