{
  "id": 6193,
  "url": "https://arxiv.org/abs/2604.07009v1",
  "title": "CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging",
  "summary": "Ensuring fairness in machine learning predictions is a critical challenge, especially when models are deployed in sensitive domains such as credit scoring, healthcare, and criminal justice. While many fairness interventions rely on data preprocessing or algorithmic constraints during training, these approaches often require full control over the model architecture and access to protected attribute information, which may not be feasible in real-world systems. In this paper, we propose Counterfact",
  "authors": "Irina Arévalo, Marcos Oliva",
  "category": "research",
  "topics": "bias-fairness,healthcare",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-08T12:32:16.000Z",
  "fetched_at": "2026-07-14T16:32:20.054Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/6193",
  "original_url": "https://arxiv.org/abs/2604.07009v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}