Diagrammatization: Rationalizing with diagrammatic AI explanations for abductive reasoning on hypotheses
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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.
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