Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features?

Concept Bottleneck Models (CBMs) are regarded as inherently interpretable because they first predict a set of human-defined concepts which are used to predict a task label. For inherent interpretability to be fully realised, and ensure trust in a model's output, it's desirable for concept predictions to use semantically meaningful <PRE_TAG>input features</POST_TAG>. For instance, in an image, pixels representing a broken bone should contribute to predicting a fracture. However, current literature suggests that concept predictions often rely on irrelevant input features. We hypothesise that this occurs when dataset labels include inaccurate concept annotations, or the relationship between input features and concepts is unclear. In general, the effect of dataset labelling on concept representations remains an understudied area. In this paper, we demonstrate that CBMs can learn to map concepts to semantically meaningful <PRE_TAG>input features</POST_TAG>, by utilising datasets with a clear link between the input features and the desired <PRE_TAG>concept predictions</POST_TAG>. This is achieved, for instance, by ensuring multiple concepts do not always co-occur and, therefore provide a clear training signal for the CBM to distinguish the relevant <PRE_TAG>input features</POST_TAG> for each concept. We validate our hypothesis on both synthetic and real-world image datasets, and demonstrate under the correct conditions, CBMs can learn to attribute semantically meaningful <PRE_TAG>input features</POST_TAG> to the correct concept predictions.