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In this 50-minute lecture, UC Berkeley professor Bin Yu explores the concept of Veridical Data Science as a foundation for developing Trustworthy AI systems. Examine the theoretical aspects of creating reliable and verifiable artificial intelligence through data-driven approaches. Learn about methodologies that ensure AI systems produce accurate, interpretable, and ethically sound results based on rigorous data science principles. Part of the Theoretical Aspects of Trustworthy AI series presented by the Simons Institute.
