Explainable Artificial Intelligence

MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes

Recent advancements in post-hoc and inherently interpretable methods have markedly enhanced the explanations of black box classifier models. These methods operate either through post-analysis or by integrating concept learning during model training. …