{
  "id": 18297,
  "url": "https://arxiv.org/abs/2608.08743v1",
  "title": "A Distribution Mapping Approach to Counterfactually Fair Reinforcement Learning",
  "summary": "Reinforcement learning (RL) seeks to optimize sequential decisions to maximize population-level benefits over time. However, when deployed in high-stakes settings such as healthcare, RL decisions might systematically restrict some subpopulation's access to valuable services in a manner contrary to the values and goals of stakeholders. Counterfactual fairness (CF) offers a promising framework to address this problem based on causal reasoning. This paper develops a data preprocessing algorithm tha",
  "authors": "Jianhan Zhang, Jitao Wang, John D. Piette, Donglin Zeng, Chengchun Shi, Zhenke Wu",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-09T14:42:47.000Z",
  "fetched_at": "2026-08-11T05:10:37.351Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/18297",
  "original_url": "https://arxiv.org/abs/2608.08743v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}