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Computer Science > Machine Learning

arXiv:2109.04518 (cs)
[Submitted on 9 Sep 2021]

Title:Unsupervised Causal Binary Concepts Discovery with VAE for Black-box Model Explanation

Authors:Thien Q. Tran, Kazuto Fukuchi, Youhei Akimoto, Jun Sakuma
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Abstract: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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2109.04518 [cs.LG]
  (or arXiv:2109.04518v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2109.04518

Submission history

From: Thien Q. Tran [view email]
[v1] Thu, 9 Sep 2021 19:06:53 UTC (8,582 KB)
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