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。其中基于反事实推理的方法通过模拟特征变化并观察对预测结果的影响。本文提出将这一模拟过程视为创造特定知识的来源，这些知识可被存储并在后续以不同方式使用。该过程在加性模型中得以阐明，特别是在朴素贝叶斯分类器案例中，并展示出该分类器在此应用场景中的有趣特性。