Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation
This addresses the need for interpretable AI explanations for users, though it is incremental in combining existing techniques for concept discovery.
The paper tackles the problem of explaining black-box classifiers by discovering high-level binary concepts in an unsupervised manner, and demonstrates that their method can discover useful concepts for explanation across multiple datasets.
We aim to explain a black-box classifier with the form: `data X is classified as class Y because X \textit{has} A, B and \textit{does not have} C' in which A, B, and C are high-level concepts. The challenge is that we have to discover in an unsupervised manner a set of concepts, i.e., A, B and C, that is useful for the explaining the classifier. We first introduce a structural generative model that is suitable to express and discover such concepts. We then propose a learning process that simultaneously learns the data distribution and encourages certain concepts to have a large causal influence on the classifier output. Our method also allows easy integration of user's prior knowledge to induce high interpretability of concepts. Using multiple datasets, we demonstrate that our method can discover useful binary concepts for explanation.