The Decoupling Concept Bottleneck Model.

The Concept Bottleneck Model (CBM) is an interpretable neural network that leverages high-level concepts to explain model decisions and conduct human-machine interaction. However, in real-world scenarios, the deficiency of informative concepts can impede the model's interpretability and subsequent interventions. This paper proves that insufficient concept information can lead to an inherent dilemma of concept and label distortions in CBM.