Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability

• URL: http://arxiv.org/abs/2510.00773v1
• Date: Wed, 01 Oct 2025 11:11:18 GMT
• Title: Uncertainty-Aware Concept Bottleneck Models with Enhanced Interpretability
• Authors: Haifei Zhang, Patrick Barry, Eduardo Brandao,
• Abstract summary: Concept Bottleneck Models (CBMs) first embed images into a set of human-understandable concepts.<n>CBMs offer a semantically meaningful and interpretable classification pipeline.<n>CBMs often sacrifice predictive performance compared to end-to-end convolutional neural networks.
• Score: 2.624902795082451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the context of image classification, Concept Bottleneck Models (CBMs) first embed images into a set of human-understandable concepts, followed by an intrinsically interpretable classifier that predicts labels based on these intermediate representations. While CBMs offer a semantically meaningful and interpretable classification pipeline, they often sacrifice predictive performance compared to end-to-end convolutional neural networks. Moreover, the propagation of uncertainty from concept predictions to final label decisions remains underexplored. In this paper, we propose a novel uncertainty-aware and interpretable classifier for the second stage of CBMs. Our method learns a set of binary class-level concept prototypes and uses the distances between predicted concept vectors and each class prototype as both a classification score and a measure of uncertainty. These prototypes also serve as interpretable classification rules, indicating which concepts should be present in an image to justify a specific class prediction. The proposed framework enhances both interpretability and robustness by enabling conformal prediction for uncertain or outlier inputs based on their deviation from the learned binary class-level concept prototypes.

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