{
  "id": 16135,
  "url": "https://arxiv.org/abs/2607.22045v2",
  "title": "CEL: Comprehensive Counterfactual Explanations Library and Benchmark",
  "summary": "Counterfactual explanations are a prominent approach in explainable artificial intelligence (xAI), providing actionable guidance on what input changes would alter a model's prediction to a desired outcome. While early methods primarily focused on minimal feature changes, recent work incorporates additional properties such as sparsity, actionability and plausibility. Despite this progress, fair and systematic evaluation remains challenging. Existing studies often rely on different data splits, pr",
  "authors": "Oleksii Furman, Łukasz Lenkiewicz, Marcel Musiałek, Maciej Zięba",
  "category": "research",
  "topics": "transparency",
  "orgs": "xai",
  "regions": null,
  "published_at": "2026-07-24T07:21:27.000Z",
  "fetched_at": "2026-08-04T05:10:21.797Z",
  "source_slug": "x-arxiv-fairness-query",
  "source_name": "arXiv fairness query",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/16135",
  "original_url": "https://arxiv.org/abs/2607.22045v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}