Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models

ArXi:2605.06640v1 Announce Type: new *Concept-based explanations* offer a promising approach for explaining the predictions of deep neural networks in terms of high-level, human-understandable concepts. However, existing methods either do not establish a causal connection between the concepts and model predictions or are limited in expressivity and only able to infer causal explanations involving single concepts.