Model- and data-agnostic justifications with a fortiori case-based argumentation

Abstract

AF-CBA is an example-based approach to XAI that draws on the case-based argumentation tradition in AI & Law. It means to explain binary classifications made by an opaque machine-learning model by presenting an argument graph to the user, which represents an argument game about the classification of a case on the basis of precedents derived from labelled data used in the training phase of the classifier. We improve the robustness of this method by modifying it to better handle inconsistent labelling and evaluate an alternative setup that does not require access to the labelled data by using earlier predictions instead.