{
  "id": 16643,
  "url": "https://arxiv.org/abs/2608.04365",
  "title": "Manipulation-Proof Oblivious Audits against Deceptive Model Providers",
  "summary": "arXiv:2608.04365v1 Announce Type: cross Abstract: Audits have emerged as a critical instrument for algorithmic governance, providing a mechanism for external scrutiny and governance of machine learning models. However, ensuring the integrity of such assessments remains a challenging issue. For instance in regulatory contexts, audits are typically declared or easily detected, thus enabling model providers to manipulate the process, whether intentionally or inadvertently. This vulnerability is par",
  "authors": "Augustin Godinot, Sofiane Azogagh, Julien Ferry, S\\'ebastien Gambs",
  "category": "research",
  "topics": "regulation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-08-06T04:00:00.000Z",
  "fetched_at": "2026-08-06T05:10:11.148Z",
  "source_slug": "arxiv-cscy",
  "source_name": "arXiv cs.CY",
  "source_homepage": "https://arxiv.org/list/cs.CY/recent",
  "ethics_ai_record_url": "https://ethics.ai/record/16643",
  "original_url": "https://arxiv.org/abs/2608.04365",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}