Confidence-Aware Escalation in Enterprise AI Governance: A Technical Framework
TL;DR
The Confidence-Aware Escalation (CAE) framework is formalized, which integrates quantified uncertainty into escalation decisions via conformal prediction theory, and achieves high automation rates while maintaining near-theoretical coverage guarantees and enabling transparent governance of false escalation risk.
Abstract
The deployment of artificial intelligence systems in enterprise risk management presents a novel governance challenge: determining which AI-generated risk assessments carry sufficient confidence for automated action and which warrant human review. This is the confidence-conditioned escalation problem, where uncertainty quantification governs the boundary between automated AI and human intervention. Current enterprise governance practices rely on categorical automation rules that conflate risk category classification with model confidence, creating a critical epistemic gap. This paper formalizes the Confidence-Aware Escalation (CAE) framework, which integrates quantified uncertainty into escalation decisions via conformal prediction theory. Conformal prediction provides distribution-free, finite-sample coverage guarantees, enabling governance policies to be specified in terms of verifiable error rate bounds rather than heuristic confidence thresholds. The CAE framework classifies AI outputs into three automation tiers by jointly evaluating prediction-set cardinality and risk category. A three-layer governance architecture comprising inference production, human oversight, and regulatory compliance is proposed, supported by structured stakeholder roles: Risk Owners, Model Stewards, and Executives. Adaptive feedback learning pipelines maintain coverage guarantees under concept drift without reward-hacking incentives. A policy-driven fairness monitoring protocol resolves mathematical incompatibility among fairness criteria through organizational policy specification rather than technical compromise. Empirical validation on a publicly available financial risk benchmark dataset confirms that the framework achieves high automation rates while maintaining near-theoretical coverage guarantees and enabling transparent governance of false escalation risk. The framework is aligned with EU AI Act high-risk classification requirements and US Federal Reserve SR 11-7 model risk management guidance.