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Please join us for a Statistics and DSI joint colloquium.

Thursday, January 16
2:00pm – 3:00pm
Jones Hall 303
5747 S Ellis Avenue
Chicago, IL 60637

Title: Statistical Foundations of Trustworthy AI Engineering

Abstract: My research has been almost entirely devoted to a single question: How can we build trustworthy systems from untrustworthy AI algorithms? Answering this question is difficult because modern AI models can be wrong in unpredictable ways. From data, these models learn biases, spurious associations, and imperfect world-models that are difficult to debug due to their statistical nature. But to use AI in critical applications— from legal and financial institutions to power plants to hospitals, where safety, and lives are at stake—we need trust. Part of what holds us back is a lack of formally grounded but practical statistical methodology for ensuring that we are able to use AI reliably, even when the underlying model may have flaws. The talk will have two halves. In the first half, I will discuss conformal risk control, a statistical framework for reliable decision-making using black-box models. In the second half, I will discuss AI evaluations for aligning AI with human preferences and safety. I will focus both on the foundational statistical methodology underlying these techniques and also the large-scale deployments that have resulted, and the opportunities for future research that arise.

Bio: Department of Electrical Engineering and Computer Science, University of California, Berkeley. I am broadly interested in the use of black-box machine learning models for decision-making and statistical inference, as well as cross-disciplinary research in imaging, medicine, and biology. I’m especially excited about bridging modern AI systems with statistics to ensure their effective and responsible use. See here for more.

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