{
  "id": 3392,
  "url": "https://arxiv.org/abs/2606.00656v1",
  "title": "Demystifying the Optimal Fair Classifier in Multi-Class Classification",
  "summary": "Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models. Most existing bias mitigation techniques are tailored to binary settings, and the presence of multi-dimensional outputs and complex fairness mechanisms makes their extension to multi-class scenarios neither straightforward nor effective. In this paper, we investigate two fundamental, unresolv",
  "authors": "Li Zhang, Yuyuan Li, XiaoHua Feng, Jiaming Zhang, Fengyuan Yu, Chaochao Chen",
  "category": "research",
  "topics": "bias-fairness,finance-investment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-30T10:00:52.000Z",
  "fetched_at": "2026-07-14T16:30:14.368Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3392",
  "original_url": "https://arxiv.org/abs/2606.00656v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}