Model Agnostic Local Explanations of Reject
André Artelt, Roel Visser, Barbara Hammer · 2022
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.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 reject options is still an open problem.We propose a model-agnostic method for locally explaining reject options by means of interpretable models and counterfactual explanations.