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Explainable AI (XAI) has been proposed as a valuable tool to assist indownstream tasks involving human and AI collaboration. Perhaps the mostpsychologically valid XAI techniques are case based approaches which display'whole' exemplars to explain the predictions of black box AI systems. However,for such post hoc XAI methods dealing with images, there has been no attempt toimprove their scope by using multiple clear feature 'parts' of the images toexplain the predictions while linking back to relevant cases in the trainingdata, thus allowing for more comprehensive explanations that are faithful tothe underlying model. Here, we address this gap by proposing two generalalgorithms (latent and super pixel based) which can isolate multiple clearfeature parts in a test image, and then connect them to the explanatory casesfound in the training data, before testing their effectiveness in a carefullydesigned user study. Results demonstrate that the proposed approachappropriately calibrates a users feelings of 'correctness' for ambiguousclassifications in real world data on the ImageNet dataset, an effect whichdoes not happen when just showing the explanation without feature highlighting.

Advancing Post Hoc Case Based Explanation with Feature Highlighting