Contrastive Concept Importance: Explaining Pairwise Class Decisions Through Automatically Extracted Concept Representations

Concept-based explanations are a prevalent way to explain the decisions of complex black-box methods through semantically meaningful, human interpretable concepts. To attribute the contribution of such concepts to a model's decisions, feature attribution methods are used to quantify how strongly eac