{
  "id": 17769,
  "url": "https://arxiv.org/abs/2509.16462",
  "title": "Let's Unlearn Stereotypes Before Decision-Making: Assessing the Impact of Intrinsic Bias Mitigation on Downstream Fairness in LLMs",
  "summary": "arXiv:2509.16462v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly used in high-stakes decision-making systems, where biased predictions can reinforce social and economic disparities. Although prior work has examined intrinsic representational bias and unfair downstream behavior separately, it remains unclear whether mitigating intrinsic bias leads to fairer downstream outcomes. We introduce Fairness-Aware Concept Unlearning (FACU), a model-level mitigation m",
  "authors": "Mina Arzaghi, Alireza Dehghanpour Farashah, Florian Carichon, Jean-Fran\\c{c}ois Plante, Golnoosh Farnadi",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-10T04:00:00.000Z",
  "fetched_at": "2026-08-10T05:10:00.488Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/17769",
  "original_url": "https://arxiv.org/abs/2509.16462",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}