Fairwashing and the Folly of ML Solutionism with Zachary Lipton

Today we're joined by Zachary Lipton, Assistant Professor in the Tepper School of Business at Carnegie Mellon University and affiliate faculty in the Machine Learning Department (MLD) and Heinz school of Public Policy. With an overarching theme of data quality and interpretation, Zachary's research and work is focused on machine learning in healthcare, with the goal of not replacing doctors, but to assist through an understanding of the diagnosis and treatment process. Zachary is also working on the broader question of fairness and ethics in machine learning systems across multiple industries. We delve into these topics today, discussing supervised learning in the medical field, guaranteed robustness under distribution shifts, the concept of ‘fairwashing', how there is insufficient language in machine learning to encompass abstract ethical behavior, and much, much more.