01953nas a2200229   4500008004100000020002200041245008200063210006900145260005200214520119500266100002301461700001901484700001701503700002001520700001701540700001901557700001901576700001701595700002201612700002201634856006701656       2020                        eng d  a978-3-030-46150-800aBlack Box Explanation by Learning Image Exemplars in the Latent Feature Space0 aBlack Box Explanation by Learning Image Exemplars in the Latent   aChambSpringer International Publishingc2020//3 aWe 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.1 aGuidotti, Riccardo1 aMonreale, Anna1 aMatwin, Stan1 aPedreschi, Dino1 aBrefeld, Ulf1 aFromont, Elisa1 aHotho, Andreas1 aKnobbe, Arno1 aMaathuis, Marloes1 aRobardet, Céline  uhttps://link.springer.com/chapter/10.1007/978-3-030-46150-8_12