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While machine learning models are usually assumed to always output aprediction, there also exist extensions in the form of reject options whichallow the model to reject inputs where only a prediction with an unacceptablylow certainty would be possible. With the ongoing rise of eXplainable AI, a lotof methods for explaining model predictions have been developed. However,understanding why a given input was rejected, instead of being classified bythe model, is also of interest. Surprisingly, explanations of rejects have notbeen considered so far.  We propose to use counterfactual explanations for explaining rejects andinvestigate how to efficiently compute counterfactual explanations of differentreject options for an important class of models, namely prototype-basedclassifiers such as learning vector quantization models.

Explaining Reject Options of Learning Vector Quantization Classifiers