On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach

Understand

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.