  The application of machine learning based decision making systems in safety
critical areas requires reliable high certainty predictions. Reject options are
a common way of ensuring a sufficiently high certainty of predictions made by
the system. While being able to reject uncertain samples is important, it is
also of importance to be able to explain why a particular sample was rejected.
However, explaining general reject options is still an open problem. We propose
a model agnostic method for locally explaining arbitrary reject options by
means of interpretable models and counterfactual explanations.
