Although a recent shift has been made in the field of predictive process monitoring to use models from the explainable artificial intelligence field, the evaluation still occurs mainly through performance-based metrics, thus not accounting for the actionability and implications of the explanations. In this paper, we define explainability through the interpretability of the explanations and the faithfulness of the explainability model in the field of process outcome prediction. The introduced properties are analysed along the event, case, and control flow perspective which are typical for a process-based analysis. This allows comparing inherently created explanations with post-hoc explanations. We benchmark seven classifiers on thirteen real-life events logs, and these cover a range of transparent and non-transparent machine learning and deep learning models, further complemented with explainability techniques. Next, this paper contributes a set of guidelines named X-MOP which allows selecting the appropriate model based on the event log specifications, by providing insight into how the varying preprocessing, model complexity and explainability techniques typical in process outcome prediction influence the explainability of the model.

翻译：尽管预测性流程监控领域近期已转向使用可解释人工智能领域的模型，但其评估仍主要基于性能指标，未能涵盖解释的可操作性和影响。本文在流程结果预测领域，通过解释的可理解性及可解释性模型的保真度来定义可解释性。我们沿流程分析典型的"事件-案例-控制流"三重视角对所引入的特性进行分析，从而能够对比原生解释与事后解释方法。我们基于十三个真实事件日志对七种分类器进行基准测试，这些分类器涵盖透明与非透明的机器学习和深度学习模型，并进一步辅以可解释性技术。此外，本文提出了一套名为X-MOP的指南框架，通过揭示流程结果预测中典型的预处理方式、模型复杂度及可解释性技术如何影响模型的可解释性，从而根据事件日志特征选择最合适的模型。