发表机构
SKEMA Business School; KU Leuven（SKEMA商学院; 鲁汶大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究预测过程监控中深度学习模型黑箱问题，提出基于控制流感知分割算法的局部事后可解释性方法，能计算段级SHAP解释，在合成及真实数据集上评估验证，提升模型可解释性。
AI 中文摘要
预测过程监控通过预测当前案例的未来状态或结果来支持运营业务流程的优化和控制。虽然深度神经网络通过对事件日志中的顺序依赖关系建模在这些任务上取得了强大性能，但其黑箱性质限制了信任和实际应用。特征归因方法常被用于解决此问题，但直接应用存在困境：事件级归因对长轨迹计算复杂度高，而基于聚合轨迹表示的解释常无法捕捉潜在控制流动态。为解决此问题，我们提出一种用于结果预测的深度神经网络局部事后可解释性方法。该方法依赖一种控制流感知分割算法，将轨迹分割成有意义的段并支持段级SHAP解释的计算。这使得能够识别轨迹的哪些部分影响预测以及哪些变化点将案例导向预测结果。我们在具有已知过程逻辑的合成数据集上评估了所提出的分割方法，在其中可以明确验证有意义的变化点，并在来自贷款申请流程和荷兰一个市政当局行政流程的真实世界事件日志上证明了其有用性。
英文摘要
Predictive process monitoring supports the optimization and control of operational business processes by forecasting the future state or outcome of ongoing cases. While deep neural networks have achieved strong performance for these tasks by modeling sequential dependencies in event logs, their black-box nature limits trust and practical adoption. Feature attribution methods are often used to address this, but applying them directly poses a dilemma: event-level attributions impose high computational complexity for long traces, while explanations based on aggregated trace representations often fail to capture the underlying control-flow dynamics. To address this issue, we propose a local post-hoc explainability method for deep neural networks in outcome prediction. The method relies on a control-flow-aware segmentation algorithm that partitions a trace into meaningful segments and supports the computation of segment-level SHAP explanations. This makes it possible to identify which parts of a trace influence a prediction and which change points steer the case toward the predicted outcome. We assess the proposed segmentation method on a synthetic dataset with known process logic, where meaningful change points can be explicitly verified, and we demonstrate its usefulness on real-world event logs from a loan application process and an administrative process of a Dutch municipality.
