{
  "id": 3632,
  "url": "https://arxiv.org/abs/2605.27827v1",
  "title": "Operational AI Deployment Assurance: Governance-State Orchestration Under Threshold-Sensitive Deployment Conditions -- A Governance Framework for High-Stakes AI Systems",
  "summary": "AI governance frameworks increasingly emphasize fairness, transparency, accountability, and lifecycle risk management in high-stakes domains. However, many current approaches remain observational, relying on static metric reporting, post-hoc auditing, and monitoring dashboards without directly governing deployment readiness, remediation progression, escalation states, or assurance-driven deployment control. This paper introduces Operational AI Deployment Assurance (OADA), a governance framework ",
  "authors": "Khalid Adnan Alsayed",
  "category": "research",
  "topics": "bias-fairness,regulation,transparency",
  "orgs": null,
  "regions": null,
  "published_at": "2026-05-27T01:33:40.000Z",
  "fetched_at": "2026-07-14T16:30:23.247Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/3632",
  "original_url": "https://arxiv.org/abs/2605.27827v1",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}