  When neural networks are employed for high-stakes decision-making, it is
desirable that they provide explanations for their prediction in order for us
to understand the features that have contributed to the decision. At the same
time, it is important to flag potential outliers for in-depth verification by
domain experts. In this work we propose to unify two differing aspects of
explainability with outlier detection. We argue for a broader adoption of
prototype-based student networks capable of providing an example-based
explanation for their prediction and at the same time identify regions of
similarity between the predicted sample and the examples. The examples are real
prototypical cases sampled from the training set via our novel iterative
prototype replacement algorithm. Furthermore, we propose to use the prototype
similarity scores for identifying outliers. We compare performances in terms of
the classification, explanation quality, and outlier detection of our proposed
network with other baselines. We show that our prototype-based networks beyond
similarity kernels deliver meaningful explanations and promising outlier
detection results without compromising classification accuracy.
