Frontiers in Artificial Intelligence is a series of public lectures at Microsoft Research Cambridge featuring leading researchers in the field, focusing on the cutting edge topics at the intersection of machine learning, statistics, and artificial intelligence. Students, scientists, and engineers in academia and industry are all welcome to join us for these exciting talks and the opportunity to socialize with the Cam-bridge AI/ML community.
What Can Fair ML Learn from Economic Theories of Distributive Justice?
Hoda Heidari, ETH Zurich
Recently, a number of technical solutions have been proposed for tackling algorithmic unfairness and discrimination. I will talk about some of the connections between these proposals and to the long-established economic theories of fairness and distributive justice. In particular, I will overview the axiomatic characterization of measures of (income) inequality, and present them as a unifying framework for quantifying individual- and group-level unfairness; I will propose the use of cardinal social welfare functions as an effective method for bounding individual-level inequality; and last but not least, I will cast existing notions of algorithmic (un)fairness as special cases of economic models of equality of opportunity—through this lens, I hope to offer a better understanding of the moral assumptions underlying technical definitions of fairness.