Model Agnostic Local Explanations of Reject

André Artelt; Roel Visser; Barbara Hammer

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arXiv · 2205.07623

Model Agnostic Local Explanations of Reject

André Artelt·Roel Visser·Barbara Hammer

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Abstract

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.

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André Artelt, Roel Visser, Barbara Hammer. 2022-05-16. Model Agnostic Local Explanations of Reject. https://arxiv.org/abs/2205.07623

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