Machine learning (ML) is is becoming increasingly prevalent across industries, creating new types and new levels of risk. Managing this risk is quickly becoming the central challenge of major organizations, one that strains data science teams, legal personnel, and the C-suite alike. How organizations approach manage this risk will determine their ability to fully make use of ML and in some cases, their long-term success or failure.
However, the current approach to managing risk in ML is extremely limited and will only get organizations so far. Currently, attempts to address risk in ML are approached through the lens of explainability, with a focus on understanding the “black box” of ML models. But the future of ML risk management is going to be focused on much more than explaining the inner workings of these models.
Andrew Burt shares key lessons from past regulations focused on complex, opaque technology along with a proposal for new ways to effectively manage risk in ML.
Andrew is Managing Partner at bnh.ai, a boutique law firm focused on AI and analytics, and Chief Legal Officer at Immuta. He is also a Visiting Fellow at Yale Law School’s Information Society Project. Previously, Andrew served as Special Advisor for Policy to the head of the Federal Bureau of Investigation’s Cyber Division, where he served as lead author on the FBI’s after action report for the 2014 attack on Sony.
A leading authority on the intersection between law and technology, Andrew has published articles in The New York Times, The Financial Times, and Harvard Business Review, where he is a regular contributor.
Andrew is a term-member of the Council on Foreign Relations, a member of the Washington, D.C. and Virginia State Bars, and a certified cyber incident response handler. He holds a JD from Yale Law School and a BA with first-class honors from McGill University.
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