{
  "id": 4603,
  "url": "https://arxiv.org/abs/2605.09852v1",
  "title": "Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI",
  "summary": "Machine learning algorithms are being used in high-stakes decisions, including those in criminal justice, healthcare, credit, and employment. The research community has responded with two largely independent research fields: \\emph{algorithmic fairness}, which targets equitable outcomes, and \\emph{explainable AI} (XAI), which targets interpretable reasoning. This survey identifies and maps a novel blind spot at their intersection, which is a model that can satisfy every standard fairness criterio",
  "authors": "Gideon Popoola, John Sheppard",
  "category": "research",
  "topics": "bias-fairness,jobs-economy,healthcare,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-11T01:09:06.000Z",
  "fetched_at": "2026-07-14T16:31:08.354Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/4603",
  "original_url": "https://arxiv.org/abs/2605.09852v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}