A Combinatorial Approach to Explaining Image Classifiers

Abstract

Machine Learning (ML) models, a core component to artificial intelligence systems, often come as a black box to the user, leading to the problem of interpretability. Explainable Artificial Intelligence (XAI) is key to providing confidence and trustworthiness for machine learning-based software systems. We observe a fundamental connection between XAI and software fault localization. In this paper, we present an approach that uses BEN, a combinatorial testing-based software fault localization approach, to produce explanations for decisions made by ML models.