Interpretable models are designed to make decisions in a human-interpretable manner. Representatively, Concept Bottleneck Models (CBM) follow a two-step process of concept prediction and class prediction based on the predicted concepts. CBM provides explanations with high-level concepts derived from concept predictions; thus, reliable concept predictions are important for trustworthiness. In this study, we address the ambiguity issue that can harm reliability. While the existence of a concept can often be ambiguous in the data, CBM predicts concepts deterministically without considering this ambiguity. To provide a reliable interpretation against this ambiguity, we propose Probabilistic Concept Bottleneck Models (ProbCBM). By leveraging probabilistic concept embeddings, ProbCBM models uncertainty in concept prediction and provides explanations based on the concept and its corresponding uncertainty. This uncertainty enhances the reliability of the explanations. Furthermore, as class uncertainty is derived from concept uncertainty in ProbCBM, we can explain class uncertainty by means of concept uncertainty. Code is publicly available at https://github.com/ejkim47/prob-cbm.

翻译：可解释模型旨在以人类可理解的方式进行决策。典型地，概念瓶颈模型（CBM）遵循概念预测和基于预测概念的类别预测这两步流程。CBM利用从概念预测中衍生出的高层概念提供解释；因此，可靠的概念预测对可信赖性至关重要。在本研究中，我们解决可能损害可靠性的歧义性问题。尽管数据中概念的存在往往存在歧义，但CBM以确定性方式预测概念，未考虑这种歧义性。为针对这一歧义性提供可靠的解释，我们提出了概率概念瓶颈模型（ProbCBM）。通过利用概率概念嵌入，ProbCBM对概念预测中的不确定性进行建模，并基于概念及其对应不确定性提供解释。这种不确定性增强了解释的可靠性。此外，由于ProbCBM中的类别不确定性源自概念不确定性，我们能够通过概念不确定性来解释类别不确定性。代码公开于https://github.com/ejkim47/prob-cbm。