Hotelling Lecture: Bin Yu; Departments of Statistics and Electrical Engineering & Computer Sciences University of California at Berkeley

Veridical Data Science: the practice of responsible data analysis and decision-making

Tuesday, April 19, 2022

“A.I. is like nuclear energy — both promising and dangerous” — Bill Gates, 2019.

Data Science is a pillar of A.I. and has driven most of recent cutting-edge discoveries in biomedical research and beyond. In practice, Data Science has a life cycle (DSLC) that includes problem formulation, data collection, data cleaning, modeling, result interpretation and the drawing of conclusions. Human judgement calls are ubiquitous at every step of this process, e.g., in choosing data cleaning methods, predictive algorithms and data perturbations. Such judgment calls are often responsible for the “dangers” of A.I. To maximally mitigate these dangers, we developed a framework based on three core principles: Predictability, Computability and Stability (PCS). Through a workflow and documentation (in R Markdown or Jupyter Notebook) that allows one to manage the whole DSLC, the PCS framework unifies, streamlines and expands on the best practices of machine learning and statistics – taking a step forward towards veridical Data Science.

In this lecture, we will illustrate the PCS framework through the development of of iterative random forests (iRF) for predictive and stable non-linear interaction discovery and through using iRF and UK biobank data to find gene-gene interactions driving, respectively, red-hair and a heart disease called hypertrophic cariomyopathy.