Abstract

Aaron Roth: (Un)fairness in Machine Learning

In this talk, we will quickly survey some of the sources of "unfairness" in machine learning, and discuss the perspective that theory can bring to what is a messy empirical problem. We will then talk about the consequences of enforcing a family of fairness definitions that we have been calling "weakly meritocratic fairness" on the -process- of learning, in a number of settings.

Based on joint works with Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Sampath Kannan, Michael Kearns, Jamie Morgenstern, Seth Neel, Mallesh Pai, Rakesh Vohra, Steven Wu