AI 中文总结
针对输入不确定性下的线性“先预测后优化”决策管道，提出局部违规认证框架，可通过单次优化求解直接计算局部故障风险，在电力调度系统上验证，成本低且评估精确可审计。
AI 中文摘要
结合预测机器学习模型与下游优化软件的数据驱动决策管道，越来越多地用于高风险运营决策。验证这些决策的安全性、公平性和可靠性至关重要，但传统场景生成方法依赖重复随机测试，当故障事件罕见时，计算成本会变得过高，且几乎无法洞察故障发生的原因。我们提出一种专为输入不确定性下的线性决策管道设计的局部违规认证框架。我们从数学上证明，标准采样方法在罕见违规场景下效率低下，从而推动了直接结构方法的开发。通过分析已部署管道的固定决策边界，我们表明，可通过单次优化求解直接以闭式形式计算局部故障风险。此外，我们引入了一种精确采样程序和闭式风险统计量，该统计量可提供特征级归因（识别哪些输入特征对潜在违规贡献最大），无需重复随机试验或复杂采样算法。我们在受排放法规约束的经济电力调度系统上验证了我们的方法，以仅为传统计算成本的一小部分，提供精确、可审计的风险评估。
英文摘要
Data-driven decision pipelines combining predictive machine learning models with downstream optimization software are increasingly used to make high-stakes operational decisions. Certifying the safety, fairness, and reliability of these decisions is essential, yet traditional scenario generation methods rely on repeated random testing, which becomes computationally prohibitive when failure events are rare and offers little insight into why failures occur. We present a framework for local violation certification designed specifically for linear decision pipelines under input uncertainty. We mathematically demonstrate that standard sampling methods fail efficiently for rare violations, motivating a direct structural approach. By analyzing the fixed decision boundary of a deployed pipeline, we show that the local risk of failure can be calculated directly in closed form using a single optimization solve. Furthermore, we introduce an exact sampling procedure and closed-form risk statistics that provide feature-level attributions (identifying which input characteristics contribute most to potential non-compliance) without requiring repetitive random trials or complex sampling algorithms. We demonstrate our approach on an economic power dispatch system subject to emissions regulations, delivering precise, auditable risk assessments at a fraction of the traditional computational cost.
Comments24 pages, 3 figures
