We propose answer-set programs that specify and compute counterfactual interventions as a basis for causality-based explanations to the outcomes from classification models. They can be applied with black-box models, and also with models that can be specified as logic programs, such as rule-based classifiers. The main focus is on the specification and computation of maximum-responsibility counterfactual explanations, with responsibility becoming an explanation score for features of entities under classification. We also extend the programs to bring into the picture semantic or domain knowledge. We show how the approach could be extended by means of probabilistic methods, and how the underlying probability distributions could be modified through the use of constraints.

中文翻译：

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分类反事实解释的声明性方法

我们提出了答案集程序，该程序指定和计算反事实干预，作为对分类模型结果的基于因果关系的解释的基础。它们可以应用于黑盒模型，也可以应用于可以指定为逻辑程序的模型，例如基于规则的分类器。主要关注最大责任反事实解释的规范和计算，责任成为分类实体特征的解释分数。我们还扩展了程序以引入图片语义或领域知识。我们展示了如何通过概率方法扩展该方法，以及如何通过使用约束来修改潜在的概率分布。