Provable concept learning for interpretable predictions using variational autoencoders

TL;DR

This paper introduces CLAP, a VAE-based framework that provably identifies high-level, unknown concepts for interpretable predictions, ensuring both interpretability and optimal accuracy in safety-critical applications.

Contribution

The paper presents a probabilistic model that guarantees the identification of ground-truth concepts while maintaining classification performance.

Findings

CLAP successfully identifies ground-truth concepts in synthetic datasets.

CLAP achieves promising interpretability results on Chest X-Ray data.

Theoretical proof of concept identification under the generative model.

Abstract

In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations revolve around pixel-based attributions or use previously known concepts. In this paper we aim to provide explanations by provably identifying \emph{high-level, previously unknown ground-truth concepts}. To this end, we propose a probabilistic modeling framework to derive (C)oncept (L)earning and (P)rediction (CLAP) -- a VAE-based classifier that uses visually interpretable concepts as predictors for a simple classifier. Assuming a generative model for the ground-truth concepts, we prove that CLAP is able to identify them while attaining optimal classification accuracy. Our experiments on synthetic datasets verify that CLAP identifies distinct ground-truth concepts on synthetic datasets and yields promising results on the medical Chest X-Ray dataset.