Many visualizations have been developed for explainable AI (XAI), but they often require further reasoning by users to interpret. We argue that XAI should support abductive reasoning - inference to the best explanation - with diagrammatic reasoning to convey hypothesis generation and evaluation. Inspired by Peircean diagrammatic reasoning and the 5-step abduction process, we propose Diagrammatization, an approach to provide diagrammatic, abductive explanations based on domain hypotheses. We implemented DiagramNet for a clinical application to predict diagnoses from heart auscultation, and explain with shape-based murmur diagrams. In modeling studies, we found that DiagramNet not only provides faithful murmur shape explanations, but also has better prediction performance than baseline models. We further demonstrate the usefulness of diagrammatic explanations in a qualitative user study with medical students, showing that clinically-relevant, diagrammatic explanations are preferred over technical saliency map explanations. This work contributes insights into providing domain-conventional abductive explanations for user-centric XAI.

该研究提出了Diagrammatization方法，它基于领域假设提供了图解的归纳解释，该方法使用Peircean图解推理和5步缩写过程进行推理。它还对临床应用进行了建模研究，演示了虔诚的、图解式的说明对于技术显著性图的说明更受欢迎。

图式化：利用图式化 AI 解释进行假设性推理的合理化