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Beyond Explainability: A Practical Guide to Managing Risk in Machine Learning Models Andrew Burt Stuart Shirrell Chief Privacy Officer and Legal Engineer, Legal Engineer, Immuta Immuta Brenda Leong Xiangnong (George) Wang Senior Counsel and Director of 2018 Immuta Scholar;. Strategy, Future of Privacy Forum Candidate, Yale Law School How can we govern a technology its creators can't fully explain? 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 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. According to Bain & Company, investments in automation in the US alone will approach $8 trillion in the com- ing years, many premised on recent advances in But these advances have far outpaced the legal and ethical frameworks for managing this technology.
Beyond Explainability v1.0 Page 3 Key Objectives & The Three Lines of Defense Projects that involve ML will be on the strongest footing with clear objectives from the start.
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