Improving Intervention Efficacy via Concept Realignment in Concept Bottleneck Models

TL;DR

This paper introduces a trainable concept realignment module for Concept Bottleneck Models that leverages concept relations to improve intervention efficiency, reducing the number of interventions needed for accurate classification.

Contribution

The paper proposes a novel realignment module that enhances intervention effectiveness in CBMs by utilizing concept relations, without altering existing model architectures.

Findings

Significant reduction in interventions needed for target performance.

Improved concept prediction accuracy with fewer interventions.

Easy integration into existing CBM architectures.

Abstract

Concept Bottleneck Models (CBMs) ground image classification on human-understandable concepts to allow for interpretable model decisions. Crucially, the CBM design inherently allows for human interventions, in which expert users are given the ability to modify potentially misaligned concept choices to influence the decision behavior of the model in an interpretable fashion. However, existing approaches often require numerous human interventions per image to achieve strong performances, posing practical challenges in scenarios where obtaining human feedback is expensive. In this paper, we find that this is noticeably driven by an independent treatment of concepts during intervention, wherein a change of one concept does not influence the use of other ones in the model's final decision. To address this issue, we introduce a trainable concept intervention realignment module, which leverages concept relations to realign concept assignments post-intervention. Across standard, real-world benchmarks, we find that concept realignment can significantly improve intervention efficacy; significantly reducing the number of interventions needed to reach a target classification performance or concept prediction accuracy. In addition, it easily integrates into existing concept-based architectures without requiring changes to the models themselves. This reduced cost of human-model collaboration is crucial to enhancing the feasibility of CBMs in resource-constrained environments. Our code is available at: https://github.com/ExplainableML/concept_realignment.