Generating Likely Counterfactuals Using Sum-Product Networks

Due to user demand and recent regulation (GDPR, AI Act), decisions made by AI systems need to be explained. These decisions are often explainable only post hoc, where counterfactual explanations are popular. The question of what constitutes the best counterfactual explanation must consider multiple aspects, where "distance from the sample" is the most common. We argue that this requirement frequently leads to explanations that are unlikely and, therefore, of limited value. Here, we present a system that provides high-likelihood explanations. We show that the search for the most likely explanations satisfying many common desiderata for counterfactual explanations can be modeled using mixed-integer optimization (MIO). In the process, we propose an MIO formulation of a Sum-Product Network (SPN) and use the SPN to estimate the likelihood of a counterfactual, which can be of independent interest. A numerical comparison against several methods for generating counterfactual explanations is provided.