Title: Towards Reasonable Concept Bottlenecks
Primary area: interpretability and explainable AI
Keywords: Concept Bottleneck Models, Interpretability, Concepts, Reasoning, Explainable AI, Interventions, Leakage

Abstract: We propose a novel, flexible, and efficient  framework for designing Concept Bottleneck Models (CBMs) that enables practitioners 
to explicitly encode any of their prior knowledge and beliefs about the concept-concept ($C-C$) and concept-task ($C \to Y$) relationships into the model reasoning. 
The resulting **C**oncept **REA**soning **M**odels (CREAMs) architecturally encode potentially sparse $C \to Y$ relationships, as well as various types of $C-C$  relationships such as mutual exclusivity, hierarchical associations, and/or correlations. 
Moreover, CREAM can include a regularized side-channel to complement the potentially incomplete concept sets, achieving competitive task performance while encouraging predictions to be concept-grounded. 
Our experiments show that, without additional computational overhead, the CREAM designs: (i) allow for efficient and accurate interventions by avoiding leakage; and (ii)  achieve task performance on par with black-box models.

TL;DR: We propose a flexible and efficient framework for incorporating and supplementing prior knowledge in CBMs.