{
  "id": 7371,
  "url": "https://arxiv.org/abs/2603.11140v1",
  "title": "Procedural Fairness via Group Counterfactual Explanation",
  "summary": "Fairness in machine learning research has largely focused on outcome-oriented fairness criteria such as Equalized Odds, while comparatively less attention has been given to procedural-oriented fairness, which addresses how a model arrives at its predictions. Neglecting procedural fairness means it is possible for a model to generate different explanations for different protected groups, thereby eroding trust. In this work, we introduce Group Counterfactual Integrated Gradients (GCIG), an in-proc",
  "authors": "Gideon Popoola, John Sheppard",
  "category": "research",
  "topics": "bias-fairness",
  "orgs": null,
  "regions": null,
  "published_at": "2026-03-11T16:38:59.000Z",
  "fetched_at": "2026-07-14T16:33:12.388Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/7371",
  "original_url": "https://arxiv.org/abs/2603.11140v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}