Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition

ArXi:2604.24170v1 Announce Type: new Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epistemic uncertainty (reducible model underspecification) with aleatoric uncertainty (irreducible input ambiguity). This makes concept-level uncertainty hard to interpret and, importantly, hard to act upon. We