TRACE：用于测量生产型AI系统中可解释性债务的治理框架
TRACE: A Governance Framework for Measuring Explainability Debt in Production AI Systems
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中文总结 AI 辅助
该研究针对生产型AI系统的可解释性债务问题，提出含七种工具的TRACE治理框架，通过12个月欺诈检测系统案例验证其可预测性与风险识别能力，为欧盟AI法案合规提供定量架构。
中文摘要 AI 辅助
部署在高风险领域的生产型AI系统会积累一种现有监控框架无法检测到的治理负债：当监管机构、审计师或受影响个人要求问责时，系统逐步丧失解释单个决策的能力。我们引入TRACE（Transparency, Risk, Accountability, Compliance, and Explainability，即透明度、风险、问责制、合规性与可解释性），这是一个包含七种工具的治理框架，用于测量、跟踪和修复生产型AI系统中的可解释性债务。基础工具为可解释性债务评分（Explainability Debt Score, EDS），用于量化任意时刻低于治理定义的可解释性置信阈值的生产决策比例；补充工具包括用于违规预测的债务积累率跟踪器（Debt Accumulation Rate Tracker for breach forecasting, DART）、用于日常治理的场景健康与完整性验证器（Scenario Health and Integrity Validator for daily governance, SHIV）、用于因果归因的特征漂移评估器（Feature Drift Evaluator for causal attribution, FDE）、人工验证引擎（Human Validation Engine, HVE）、审计干预决策引擎（Audit Intervention Decision Engine, AIDE）以及用于修复的零可解释性风险优化器（Zero Explainability Risk Optimiser for remediation, ZERO）。通过对每日处理50000笔金融交易、准确率达98.46%且ROC-AUC为0.9990的生产型欺诈检测系统开展为期12个月的纵向案例研究，我们证明审计日的EDS为0.23可通过DART轨迹分析提前6个月进行统计预测（β=0.008/周，R²=0.94，95%置信区间：[0.006, 0.010]），且78%的可解释性债务集中在监管风险最高的决策类别（金额超过10000美元的交易），这种风险不对称性是系统级指标完全无法察觉的。TRACE为生产型AI部署提供了符合《欧盟人工智能法案》第13条要求的首个定量操作架构，建立了与解释生成不同的解释治理新分支。
英文摘要
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.
