{
  "id": 6768,
  "url": "https://arxiv.org/abs/2603.24218v1",
  "title": "Who Benefits from RAG? The Role of Exposure, Utility and Attribution Bias",
  "summary": "Large Language Models (LLMs) enhanced with Retrieval-Augmented Generation (RAG) have achieved substantial improvements in accuracy by grounding their responses in external documents that are relevant to the user's query. However, relatively little work has investigated the impact of RAG in terms of fairness. Particularly, it is not yet known if queries that are associated with certain groups within a fairness category systematically receive higher accuracy, or accuracy improvements in RAG system",
  "authors": "Mahdi Dehghan, Graham McDonald",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-25T11:45:52.000Z",
  "fetched_at": "2026-07-14T16:32:45.891Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6768",
  "original_url": "https://arxiv.org/abs/2603.24218v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}