[MERL Seminar Series Fall 2023] Multiplicity in Machine Learning

Flavio Calmon, Harvard, presented a talk in the MERL Seminar Series on November 7, 2023.
Abstract:
This talk reviews 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. We also present recent findings on the multiplicity cost of differentially private training methods and group fairness interventions in machine learning.
This talk is based on work published at ICML'20, NeurIPS'22, ACM FAccT'23, and NeurIPS'23.