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The Concept Bottleneck Models (CBMs) of Koh et al. [2020] provide a means toensure that a neural network based classifier bases its predictions solely onhuman understandable concepts. The concept labels, or rationales as we refer tothem, are learned by the concept labeling component of the CBM. Anothercomponent learns to predict the target classification label from thesepredicted concept labels. Unfortunately, these models are heavily reliant onhuman provided concept labels for each datapoint. To enable CBMs to behaverobustly when these labels are not readily available, we show how to equip themwith the ability to abstain from predicting concepts when the concept labelingcomponent is uncertain. In other words, our model learns to provide rationalesfor its predictions, but only whenever it is sure the rationale is correct.

Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions