A Software Fault Localization approach to Explainable Artificial Intelligence (XAI)
Abstract:
Machine learning 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 and present a technique that uses BEN, a combinatorial testing-based software fault localization approach to produce explanations for decisions made by ML models.