On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach. (arXiv:2211.01498v1 [cs.LG])

Interpretable and explainable machine learning has seen a recent surge of
interest. We focus on safety as a key motivation behind the surge and make the
relationship between interpretability and safety more quantitative. Toward
assessing safety, we introduce the concept of maximum deviation via an
optimization problem to find the largest deviation of a supervised learning
model from a reference model regarded as safe. We then show how
interpretability facilitates this safety assessment. For models including
decision trees, generalized linear …

arxiv deviation machine machine learning safety