Bin Yu, University of California, Berkeley
Title: Veridical Data Science towards Trustworthy AI
Date: Friday, September 25th, 2026
Time: 1:30PM (PDT)
Location: AQ 3003
Abstract: Data science underpins modern AI and many advances in healthcare, yet human judgment permeates every stage of the data science life cycle. These judgment calls introduce hidden uncertainties that go well beyond sampling variability and drive many of the risks associated with AI.
We introduce veridical data science, grounded in three fundamental principles鈥擯redictability, Computability, and Stability (PCS)鈥攖o make such uncertainties explicit and assessable and to aggregate reality-checked algorithms for better results. The PCS framework unifies and extends best practices in statistics and machine learning and is illustrated through healthcare applications, including identifying genetic drivers of heart disease, reducing cost of prostate cancer detection, improving uncertainty quantification beyond standard conformal prediction, and proposing, Green Shielding, a new user-centric framework for safeguarding users of AI.