On the Computational Tractability of Probabilistic Global and Local Sufficient Explanation

Abstract Explainable AI (XAI) seeks to answer the question: which features of the data led a model to make its decision? Sufficient reasons are an important concept for understanding the behaviour of machine learning models, as they identify the key characteristics responsible for the prediction of an individual instance. Recent work introduced probabilistic global sufficient reasons, extending sufficient reasons from the single-instance level to all instances in the feature domain, thereby providing a global understanding of the classifier. However, prior work on this notion has been purely theoretical, without empirical evaluation. In this paper, we aim to fill this gap by developing practical methods for computing probabilistic global sufficient reasons and evaluating them on decision trees and circuit-based models.