  While machine learning models are usually assumed to always output a
prediction, there also exist extensions in the form of reject options which
allow the model to reject inputs where only a prediction with an unacceptably
low certainty would be possible. With the ongoing rise of eXplainable AI, a lot
of methods for explaining model predictions have been developed. However,
understanding why a given input was rejected, instead of being classified by
the model, is also of interest. Surprisingly, explanations of rejects have not
been considered so far.
  We propose to use counterfactual explanations for explaining rejects and
investigate how to efficiently compute counterfactual explanations of different
reject options for an important class of models, namely prototype-based
classifiers such as learning vector quantization models.
