Responsibly Improving AI with Privacy-Sensitive Data: Principles, Theory, and Practice | Richard M. Karp Distinguished Lecture

LLMs have revolutionized the field of machine learning, but a core tenet remains: AI systems need to be built and tuned using high-quality data from the right domain. As these systems increasingly touch our daily lives, the relevant data is frequently distributed and privacy sensitive. This talk presents a framework of principles that helps bring precision to discussions of privacy and AI, and then dives into the theory and practice required to apply them in real scenarios. The lecture explores how we can unlock the power of AI while safeguarding user trust.

Brendan McMahan is a principal research scientist at Google, where he leads efforts on decentralized and privacy-preserving machine learning. His team pioneered the concept of federated learning, and continues to push the boundaries of what is possible when working with centralized and decentralized data using privacy-preserving techniques. Previously, he has worked in the fields of online learning, large-scale convex optimization, and reinforcement learning. McMahan received his PhD in computer science from Carnegie Mellon University.

Refreshments will be served at 3 p.m., before the event.

The Richard M. Karp Distinguished Lectures were created in Fall 2019 to celebrate the role of Simons Institute Founding Director Dick Karp in establishing the field of theoretical computer science, formulating its central problems, and contributing stunning results in the areas of computational complexity and algorithms. Formerly known as the Simons Institute Open Lectures, the series features visionary leaders in the field of theoretical computer science and is geared toward a broad scientific audience.

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