In this paper titled A Model-Agnostic SAT-based approach for Symbolic Explanation Enumeration we propose a generic agnostic approach allowing to generate different and complementary types of symbolic explanations. More precisely, we generate explanations to locally explain a single prediction by analyzing the relationship between the features and the output. Our approach uses a propositional encoding of the predictive model and a SAT-based setting to generate two types of symbolic explanations which are Sufficient Reasons and Counterfactuals. The experimental results on image classification task show the feasibility of the proposed approach and its effectiveness in providing Sufficient Reasons and Counterfactuals explanations.

翻译：本文题为“以模型-不可知性SAT为基础的符号解释计算方法”,我们提出了一种通用的不可知性方法,允许产生不同和互补的象征性解释类型。更准确地说,我们通过分析特征和产出之间的关系,对当地解释单一预测作出解释。我们的方法使用预测模型的假设编码和基于卫星的设置,产生两种具有充分理由和反事实的象征性解释。图像分类任务的实验结果显示拟议方法的可行性及其在提供充分理由和反事实解释方面的有效性。