🤖 AI Summary
This work addresses the limitations of existing AI governance frameworks, which rely on static metrics and post-hoc audits and thus lack the capacity for dynamic, real-time assessment of deployment readiness in high-risk systems—particularly regarding fairness discrepancies, threshold sensitivity, and remediation progress. To bridge this gap, the paper proposes the Operational AI Deployment Assurance (OADA) framework, which uniquely models governance uncertainty as an operational challenge within the deployment pipeline. OADA introduces mechanisms such as deployment assurance scores, readiness categorization, threshold stability zones, and governance escalation states to enable closed-loop, dynamic governance from evaluation to deployment. By integrating the Fairness Discrepancy Index (FDI) and FairRisk-FDI with threshold sensitivity analysis and repair-aware assurance evolution, OADA successfully identifies models deemed “compliant” by conventional metrics yet operationally unstable, offering a scalable deployment assurance paradigm for high-stakes domains like medical AI.
📝 Abstract
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.