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What Can Fair ML Learn from Economic Theories of Distributive Justice?

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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.

This talk is part of the Frontiers in Artificial Intelligence Series series.

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