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.

翻译：概念瓶颈模型（CBM）将图像分类建立在人类可理解的概念之上，从而实现可解释的模型决策。关键在于，CBM的设计天然允许人工干预——专家用户能够修改可能错位的概念选择，以可解释的方式影响模型的决策行为。然而，现有方法通常需要对每张图像进行大量人工干预才能达到良好性能，这在获取人类反馈成本高昂的场景中构成了实际挑战。本文发现，这一现象显著源于干预过程中对概念的独立处理——当一个概念被改变时，并不会影响其他概念在模型最终决策中的使用。为解决此问题，我们引入了一个可训练的概念干预重对齐模块，该模块利用概念关系在干预后重新对齐概念分配。在标准现实世界基准测试中，我们发现概念重对齐能够显著提升干预效能；大幅减少达到目标分类性能或概念预测准确率所需的干预次数。此外，该模块易于集成到现有基于概念的架构中，无需修改模型本身。这种人机协作成本的降低对于在资源受限环境中增强CBM的可行性至关重要。