论文标题
反事实解释的象征性方法
A Symbolic Approach for Counterfactual Explanations
论文作者
论文摘要
在本文中，我们提出了一种新颖的符号方法来为分类器预测提供反事实解释。与大多数解释方法相反，目标是了解数据的哪些部分以及在多大程度上有助于提出预测，反事实说明表明必须在数据中更改哪些功能才能更改此分类器预测。我们的方法是象征性的，因为它基于在等效的CNF公式中编码分类器的决策功能。在这种方法中，反事实解释被视为最小校正子集（MCS），这是知识基础赔偿中众所周知的概念。因此，这种方法利用了已经存在的MCS生成的现有和经过验证的解决方案的优势。我们对贝叶斯分类器的初步实验研究表明，这种方法在几个数据集上的潜力。
In this paper titled A Symbolic Approach for Counterfactual Explanations we propose a novel symbolic approach to provide counterfactual explanations for a classifier predictions. Contrary to most explanation approaches where the goal is to understand which and to what extent parts of the data helped to give a prediction, counterfactual explanations indicate which features must be changed in the data in order to change this classifier prediction. Our approach is symbolic in the sense that it is based on encoding the decision function of a classifier in an equivalent CNF formula. In this approach, counterfactual explanations are seen as the Minimal Correction Subsets (MCS), a well-known concept in knowledge base reparation. Hence, this approach takes advantage of the strengths of already existing and proven solutions for the generation of MCS. Our preliminary experimental studies on Bayesian classifiers show the potential of this approach on several datasets.
