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Concept bottleneck models map from raw inputs to concepts, and then fromconcepts to targets. Such models aim to incorporate pre-specified, high-levelconcepts into the learning procedure, and have been motivated to meet threedesiderata: interpretability, predictability, and intervenability. However, wefind that concept bottleneck models struggle to meet these goals. Using posthoc interpretability methods, we demonstrate that concepts do not correspond toanything semantically meaningful in input space, thus calling into question theusefulness of concept bottleneck models in their current form.

Do Concept Bottleneck Models Learn as Intended?