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This paper introduces Operational AI Deployment Assurance (OADA), a governance framework that translates fairness disagreement, subgroup instability, and threshold sensitivity into deployment-oriented assurance decisions. OADA reframes governance uncertainty as an operational concern by introducing Deployment Assurance Scores, Readiness Classifications, and Governance Escalation States. The framework is demonstrated on facial recognition systems and discussed in the context of healthcare AI, revealing that systems can appear acceptable under isolated metrics while exhibiting deployment-affecting instability.
AI systems can appear fair and performant in isolation, yet still harbor hidden instabilities that undermine real-world deployment readiness, demanding a more holistic governance approach.
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 for translating fairness disagreement, subgroup instability, threshold sensitivity, remediation outcomes, and operational uncertainty into deployment-oriented assurance decisions. Building on prior work on the Fairness Disagreement Index (FDI) and FairRisk-FDI, OADA reframes governance uncertainty as an operational concern within AI deployment pipelines rather than a byproduct of metric disagreement. The framework introduces Deployment Assurance Scores, Deployment Readiness Classifications, Threshold Stability Zones, Governance Escalation States, and remediation-aware assurance progression. These constructs support lifecycle-oriented governance decisions across high-stakes settings by connecting evaluation outputs to deployment-state interpretation, reassessment, escalation, and operational control. Through deployment-oriented evaluation across facial recognition systems, with discussion extended to healthcare AI as a representative high-stakes domain, the paper demonstrates how systems may appear acceptable under isolated fairness or performance metrics while still exhibiting instability that affects deployment readiness. The proposed framework positions operational deployment assurance as a governance layer between evaluation and real-world AI deployment.
