Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation
Abstract
We aim to explain a black-box classifier with the form: "data X is classified as class Y because X has A, B and 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 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. Finally, using multiple datasets, we demonstrate that the proposed method can discover useful concepts for explanation in this form.
Cite
Text
Tran et al. "Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I9.21195Markdown
[Tran et al. "Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/tran2022aaai-unsupervised/) doi:10.1609/AAAI.V36I9.21195BibTeX
@inproceedings{tran2022aaai-unsupervised,
title = {{Unsupervised Causal Binary Concepts Discovery with VAE for Black-Box Model Explanation}},
author = {Tran, Thien Q. and Fukuchi, Kazuto and Akimoto, Youhei and Sakuma, Jun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2022},
pages = {9614-9622},
doi = {10.1609/AAAI.V36I9.21195},
url = {https://mlanthology.org/aaai/2022/tran2022aaai-unsupervised/}
}