Counterfactual explanations suggest what should be different in the input instance to change the outcome of an AI system. When dealing with counterfactual explanations in the field of Predictive Process Monitoring, however, control flow relationships among events have to be carefully considered. A counterfactual, indeed, should not violate control flow relationships among activities (temporal background knowledege). Within the field of Explainability in Predictive Process Monitoring, there have been a series of works regarding counterfactual explanations for outcome-based predictions. However, none of them consider the inclusion of temporal background knowledge when generating these counterfactuals. In this work, we adapt state-of-the-art techniques for counterfactual generation in the domain of XAI that are based on genetic algorithms to consider a series of temporal constraints at runtime. We assume that this temporal background knowledge is given, and we adapt the fitness function, as well as the crossover and mutation operators, to maintain the satisfaction of the constraints. The proposed methods are evaluated with respect to state-of-the-art genetic algorithms for counterfactual generation and the results are presented. We showcase that the inclusion of temporal background knowledge allows the generation of counterfactuals more conformant to the temporal background knowledge, without however losing in terms of the counterfactual traditional quality metrics.

翻译：反事实解释旨在建议输入实例中应做出何种改变，以改变人工智能系统的输出结果。然而，在预测过程监控领域处理反事实解释时，必须仔细考虑事件间的控制流关系。实际上，反事实不应违反活动间的控制流关系（即时序背景知识）。在预测过程监控的可解释性研究领域，已有系列工作针对基于结果预测的反事实解释展开研究。然而，现有研究均未在生成反事实时纳入时序背景知识。本研究对可解释人工智能领域中基于遗传算法的反事实生成前沿技术进行改进，使其在运行时能够考虑一系列时序约束。我们假设这些时序背景知识已知，并调整了适应度函数以及交叉与变异算子，以确保约束得到满足。通过与现有反事实生成遗传算法的对比实验，评估了所提方法的性能并展示了实验结果。研究结果表明，纳入时序背景知识能够生成更符合时序背景知识的反事实解释，且不会在反事实传统质量指标方面有所损失。