TRACE: A Governance Framework for Measuring Explainability Debt in Production AI Systems

A new arXiv paper (2610.10957v1) introduces TRACE, a seven-instrument governance framework for measuring and remediating "Explainability Debt" in production AI systems, with the Explainability Debt Score (EDS) as its foundational metric. In a twelve-month longitudinal case study of a production fraud detection system processing 50,000 daily financial transactions at 98.46% accuracy and ROC-AUC of 0.9990, an EDS of 0.23 on audit day was statistically predictable six months in advance via DART trajectory analysis (beta = 0.008/week, R-squared = 0.94, 95% CI: [0.006, 0.010]), and 78% of the debt was concentrated in transactions above $10,000. The authors position TRACE as the first quantitative operational architecture for EU AI Act Article 13 compliance in production AI deployment.

arXiv:2610.10957v1 Announce Type: new Abstract: Production AI systems deployed in high-stakes domains accumulate a governance liability that existing monitoring frameworks fail to detect: the progressive inability to explain individual decisions when regulators, auditors, or affected individuals demand accountability. We introduce TRACE Transparency, Risk, Accountability, Compliance, and Explainability , a seven-instrument governance framework for measuring, tracking, and remediating Explainability Debt in production AI systems. The foundational instrument, the Explainability Debt Score EDS , quantifies the proportion of production decisions falling below a governance-defined explainability confidence threshold at any point in time. Complementary instruments include DART Debt Accumulation Rate Tracker for breach forecasting , SHIV Scenario Health and Integrity Validator for daily governance , FDE Feature Drift Evaluator for causal attribution , HVE Human Validation Engine , AIDE Audit Intervention Decision Engine , and ZERO Zero Explainability Risk Optimiser for remediation . Through a twelve-month longitudinal case study of a production fraud detection system processing 50,000 daily financial transactions, achieving 98.46% accuracy and ROC-AUC of 0.9990, we demonstrate that an EDS of 0.23 on audit day was statistically predictable six months in advance using DART trajectory analysis beta = 0.008/week, R-squared = 0.94, 95% CI: 0.006, 0.010 , and that 78% of Explainability Debt was concentrated in the highest-regulatory-risk decision category transactions above $10,000 , a risk asymmetry completely invisible to system-level metrics. TRACE provides the first quantitative operational architecture for EU AI Act Article 13 compliance in production AI deployment, establishing a new subdiscipline of explanation governance distinct from explanation generation.