Abstract
In this talk, I will introduce the Predictability-ComputabilityStability (PCS) framework for veridical (truthful) data science, highlighting its critical role in producing reliable and actionable insights. I will share success stories from cancer detection and cardiology, showcasing how PCS principles have guided cost effective designs and improved outcomes in these projects. Since trustworthy uncertainty quantification is indispensable for trustworthy AI, I will discuss PCS uncertainty quantification for prediction in regression and multi-class classification. PCS-UQ consists of three steps: pred-check, bootstrap, and multiplicative calibration. Through test results over 26 benchmark datasets, PCS-UQ will be shown to outperform common forms of conformal prediction in terms of width, subgroup coverage, and subgroup interval width. Finally, the multiplicative step in PCS-UQ will be shown to be a new form of conformal prediction.