  Concept bottleneck models (CBM) are a popular way of creating more
interpretable neural networks by having hidden layer neurons correspond to
human-understandable concepts. However, existing CBMs and their variants have
two crucial limitations: first, they need to collect labeled data for each of
the predefined concepts, which is time consuming and labor intensive; second,
the accuracy of a CBM is often significantly lower than that of a standard
neural network, especially on more complex datasets. This poor performance
creates a barrier for adopting CBMs in practical real world applications.
Motivated by these challenges, we propose Label-free CBM which is a novel
framework to transform any neural network into an interpretable CBM without
labeled concept data, while retaining a high accuracy. Our Label-free CBM has
many advantages, it is: scalable - we present the first CBM scaled to ImageNet,
efficient - creating a CBM takes only a few hours even for very large datasets,
and automated - training it for a new dataset requires minimal human effort.
Our code is available at https://github.com/Trustworthy-ML-Lab/Label-free-CBM.
Finally, in Appendix B we conduct a large scale user evaluation of the
interpretability of our method.
