A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
This provides a unifying moral framework for understanding algorithmic fairness, helping researchers and practitioners interpret assumptions and results, though it is incremental in linking existing concepts.
The paper maps algorithmic fairness notions to economic models of Equality of Opportunity (EOP), showing that existing definitions like predictive value parity are special cases, and proposes new fairness measures based on luck egalitarian models, with empirical illustration of welfare consequences.
We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition of algorithmic fairness, such as predictive value parity and equality of odds, can be interpreted as special cases of EOP. In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness. Most importantly, this framework allows us to explicitly spell out the moral assumptions underlying each notion of fairness, and interpret recent fairness impossibility results in a new light. Last but not least and inspired by luck egalitarian models of EOP, we propose a new family of measures for algorithmic fairness. We illustrate our proposal empirically and show that employing a measure of algorithmic (un)fairness when its underlying moral assumptions are not satisfied, can have devastating consequences for the disadvantaged group's welfare.