There are now many explainable AI methods for understanding the decisions of a machine learning model. Among these are those based on counterfactual reasoning, which involve simulating features changes and observing the impact on the prediction. This article proposes to view this simulation process as a source of creating a certain amount of knowledge that can be stored to be used, later, in different ways. This process is illustrated in the additive model and, more specifically, in the case of the naive Bayes classifier, whose interesting properties for this purpose are shown.

翻译：目前已有多种可解释人工智能方法用于理解机器学习模型的决策过程。其中基于反事实推理的方法通过模拟特征变化并观察其对预测结果的影响来实现。本文提出将这种模拟过程视为创造特定知识的来源，这些知识可以被存储并在后续以不同方式加以利用。该过程在可加模型中得到了具体阐释，特别地，以朴素贝叶斯分类器为例进行说明，并展示了该分类器在此框架下所具有的令人关注的特性。