{
  "id": 444,
  "url": "https://arxiv.org/abs/2606.30338v1",
  "title": "Sequential Fairness Auditing with Limited Output Access",
  "summary": "External evaluations are becoming increasingly central to the governance of AI systems. In practice, however, independent auditors often have limited access to deployed models and must rely on query-based interactions. Most existing fairness evaluation methods assume static datasets and fixed-sample statistical tests, making them poorly suited to real-world auditing scenarios in which evidence must be collected sequentially under query constraints. In this work, we formulate fairness auditing as",
  "authors": "Ioannis Pitsiorlas, Martha V. Sourla, Marios Kountouris",
  "category": "research",
  "topics": "bias-fairness,regulation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-06-29T14:17:33.000Z",
  "fetched_at": "2026-07-14T14:14:32.647Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/444",
  "original_url": "https://arxiv.org/abs/2606.30338v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}