Concept-based explainability methods provide insight into deep learning
systems by constructing explanations using human-understandable concepts. While
the literature on human reasoning demonstrates that we exploit relationships
between concepts when solving tasks, it is unclear whether concept-based
methods incorporate the rich structure of inter-concept relationships. We
analyse the concept representations learnt by concept-based models to
understand whether these models correctly capture inter-concept relationships.
First, we empirically demonstrate that state-of-the-art concept-based models
produce representations that lack stability and robustness, and such methods
fail to capture inter-concept relationships. Then, we develop a novel algorithm
which leverages inter-concept relationships to improve concept intervention
accuracy, demonstrating how correctly capturing inter-concept relationships can
improve downstream tasks.

使用人类可以理解的概念构建解释的基于概念的可解释性方法为深度学习系统提供了洞察力。通过分析基于概念模型学习的概念表示，我们发现现有的方法缺乏稳定性和鲁棒性，无法准确捕捉概念间的关系。因此，我们提出了一种新算法，利用概念间的关系来提高概念干预的准确性，并展示了正确捕捉概念间关系如何改善后续任务。