Date: Wednesday, May 10th, 2023
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
Title: Facets of Fair Machine Learning
This talk overviews recent information-theoretic results in two aspects of fair machine learning. First, we introduce a post-processing technique, “FairProjection,” designed to ensure group fairness in prediction and classification. This method is applicable to any classifier without requiring retraining and attains state-of-the-art performance in both accuracy and group fairness assurance in probabilistic classification. We also present converse results based on Blackwell’s “comparison of experiments” theorem that capture the limits of group-fairness assurance in classification. These results show that existing techniques (including FairProjection) can approach the optimal Pareto frontier between accuracy and group fairness in specific settings.
Second, we review the concept of predictive multiplicity in machine learning. Predictive multiplicity arises when different classifiers achieve similar average performance for a specific learning task, yet produce conflicting predictions for individual samples. We discuss a metric called “Rashomon Capacity” for quantifying predictive multiplicity in multi-class classification and present recent findings on the multiplicity cost of differentially private training methods in machine learning.
Flavio P. Calmon is an Assistant Professor of Electrical Engineering at the Harvard John A. Paulson School of Engineering and Applied Sciences. Before joining Harvard, he was the inaugural Data Science for Social Good Post-Doctoral Fellow at IBM Research in Yorktown Heights, New York. He received his Ph.D. in Electrical Engineering and Computer Science at MIT. His research develops information-theoretic tools for responsible and reliable machine learning.
Prof. Calmon has received the NSF CAREER award, faculty awards from Google, IBM, Oracle, and Amazon, the NSF-Amazon Fairness in AI award, the Harvard Data Science Initiative Bias2 award, and the Harvard Dean of Undergraduate Studies Commendation for “Extraordinary Teaching during Extraordinary Times.” He also received the inaugural Título de Honra ao Mérito (Honor to the Merit Title) given to alumni from the Universidade de Brasília (Brazil).