Label-Free Concept Bottleneck Models

Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to humanunderstandable 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. Finally, in Appendix B we conduct a large scale user evaluation of the interpretability of our method. Deep neural networks (DNNs) have demonstrated unprecedented success in a wide range of machine learning tasks such as computer vision, natural language processing, and speech recognition. However, due to their complex and deep structures, they are often regarded as black-box models that are difficult to understand and interpret. Interpretable models are important for many reasons such as creating calibrated trust in models, which means understanding when we should trust the models. Making deep learning models more interpretable is an active yet challenging research topic. Figure 1: Our proposed Label-free CBM has many desired features which existing CBMs lack, and it can transform any neural network backbone into an interpretable Concept Bottleneck Model. One approach to make deep learning more interpretable is through Concept Bottleneck Models (CBMs) (Koh et al., 2020). CBMs typically have a Concept Bottleneck Layer before the (last) fully connected layer of the neural network. The concept bottleneck layer is trained to have each neuron correspond to a single human understandable concept. This makes the final decision a linear function of interpretable concepts, greatly increasing our understanding of the decision making.