{
  "id": 11734,
  "url": "https://arxiv.org/abs/2607.16130",
  "title": "A Methodology for Auditable Trustworthiness Levels in AI Lifecycle Governance",
  "summary": "arXiv:2607.16130v1 Announce Type: new Abstract: AI governance increasingly requires judgments about whether an AI system remains adequately trustworthy over time, whether observed changes are tolerable, and how such judgments should be documented in a transparent and contestable way. Yet existing work on AI trustworthiness remains either too high-level to support lifecycle monitoring and reassessment or too narrowly metric-driven to connect with governance needs. We therefore propose a lightweig",
  "authors": "Andrea Ferrario",
  "category": "research",
  "topics": "regulation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-20T04:00:00.000Z",
  "fetched_at": "2026-07-20T05:10:09.534Z",
  "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/11734",
  "original_url": "https://arxiv.org/abs/2607.16130",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}