Explainable AI (XAI) has been proposed as a valuable tool to assist in
downstream tasks involving human and AI collaboration. Perhaps the most
psychologically 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 to
improve their scope by using multiple clear feature 'parts' of the images to
explain the predictions while linking back to relevant cases in the training
data, thus allowing for more comprehensive explanations that are faithful to
the underlying model. Here, we address this gap by proposing two general
algorithms (latent and super pixel based) which can isolate multiple clear
feature parts in a test image, and then connect them to the explanatory cases
found in the training data, before testing their effectiveness in a carefully
designed user study. Results demonstrate that the proposed approach
appropriately calibrates a users feelings of 'correctness' for ambiguous
classifications in real world data on the ImageNet dataset, an effect which
does not happen when just showing the explanation without feature highlighting.

提出了两种通用算法（潜在和超像素基础），可以从测试图像中分离出多个清晰的特征部分，并将其与训练数据中的解释性案例联系起来，通过精心设计的用户研究验证了其有效性，结果表明所提出的方法可以适当调整用户对 ImageNet 数据集中模糊分类的正确性感觉，而仅显示解释而不突出特征时无法实现该效果。