Towards more holistic interpretability: A lightweight disentangled Concept Bottleneck Model

Abstract

Concept Bottleneck Models (CBMs) enhance interpretability by predicting human-understandable concepts as intermediate representations. However, existing CBMs often suffer from input-to-concept mapping bias and limited controllability, which restricts their practical utility and undermines the reliability of concept-based strategies. To address these challenges, we propose a Lightweight Disentangled Concept Bottleneck Model (LDCBM) that automatically groups visual features into semantically meaningful components without the need for region annotations. By introducing a filter grouping loss and joint concept supervision, our method improves the alignment between visual patterns and concepts, enabling more transparent and robust decision-making. Notably, experiments on three diverse datasets demonstrate that LDCBM achieves higher concept and class accuracy, outperforming previous CBMs in both interpretability and classification performance. Complexity analysis reveals that the parameter count and FLOPs of LDCBM are less than 5% higher than those of Vanilla CBM. Furthermore, background mask intervention experiments validate the model's strong capability to suppress irrelevant image regions, further corroborating the high precision of the visual-concept mapping under LDCBM's lightweight design paradigm. By grounding concepts in visual evidence, our method overcomes a fundamental limitation of prior models and enhances the reliability of interpretable AI.

Reproductions