From Utilitarian to Rawlsian Designs for Algorithmic Fairness

Abstract: There is a lack of consensus within the literature as to how fairness' of algorithmic systems can be measured, and different metrics can often be at odds. In this paper, we approach this task by drawing on the ethical frameworks of utilitarianism and John Rawls. Informally, these two theories of distributive justice measure thegood' as either a population's sum of utility, or worst-off outcomes, respectively. We present a parameterized class of objective functions that interpolates between these two (possibly) conflicting notions of the good'. This class is shown to represent a relaxation of the Rawlsianveil of ignorance', and its sequence of optimal solutions converges to both a utilitarian and Rawlsian optimum. Several other properties of this class are studied, including: 1) a relationship to regularized optimization, 2) feasibility of consistent estimation, and 3) algorithmic cost. In several real-world datasets, we compute optimal solutions and construct the tradeoff between utilitarian and Rawlsian notions of the good'. Empirically, we demonstrate that increasing model complexity can manifest strict improvements to both measures of thegood'. This work suggests that the proper degree of fairness' can be informed by a designer's preferences over the space of induced utilitarian and Rawlsiangood'.