  We present an approach to explain the decisions of black box models for image
classification. While using the black box to label images, our explanation
method exploits the latent feature space learned through an adversarial
autoencoder. The proposed method first generates exemplar images in the latent
feature space and learns a decision tree classifier. Then, it selects and
decodes exemplars respecting local decision rules. Finally, it visualizes them
in a manner that shows to the user how the exemplars can be modified to either
stay within their class, or to become counter-factuals by "morphing" into
another class. Since we focus on black box decision systems for image
classification, the explanation obtained from the exemplars also provides a
saliency map highlighting the areas of the image that contribute to its
classification, and areas of the image that push it into another class. We
present the results of an experimental evaluation on three datasets and two
black box models. Besides providing the most useful and interpretable
explanations, we show that the proposed method outperforms existing explainers
in terms of fidelity, relevance, coherence, and stability.
