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Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models

June 19, 2018

This is the fundamental question raised by the increasing use of machine learning (ML)—a question that is quickly becoming one of the biggest challenges for data-driven organizations, data scientists, and legal personnel around the world.1 This challenge arises in various forms, and has been described in various ways by practitioners and academics alike, but all relate to the basic ability to assert a causal connection between inputs to models and how that input data impacts model output.

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Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models