  Two fundamental requirements for the deployment of machine learning models in
safety-critical systems are to be able to detect out-of-distribution (OOD) data
correctly and to be able to explain the prediction of the model. Although
significant effort has gone into both OOD detection and explainable AI, there
has been little work on explaining why a model predicts a certain data point is
OOD. In this paper, we address this question by introducing the concept of an
OOD counterfactual, which is a perturbed data point that iteratively moves
between different OOD categories. We propose a method for generating such
counterfactuals, investigate its application on synthetic and benchmark data,
and compare it to several benchmark methods using a range of metrics.
