{
  "id": 5907,
  "url": "https://arxiv.org/abs/2604.12138v3",
  "title": "Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions",
  "summary": "This position paper argues that Retrieval-Augmented Generation (RAG) systems exhibit a factual bias-optimizing for epistemic uncertainty reduction while ignoring the aleatoric uncertainty inherent in opinion-rich content. This misalignment demands a paradigm shift in RAG system design. A survey of 34 major RAG benchmarks reveals that only one addresses opinion synthesis, confirming that the bias is structural and embedded in datasets, retrieval-generation objectives, and evaluation metrics alike",
  "authors": "Aditya Agrawal, Alwarappan Nakkiran, Darshan Fofadiya, Alex Karlsson, Harsha Aduri, Aman Singh Thakur",
  "category": "research",
  "topics": "bias-fairness,safety-alignment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-13T23:39:39.000Z",
  "fetched_at": "2026-07-14T16:32:06.468Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5907",
  "original_url": "https://arxiv.org/abs/2604.12138v3",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}