From Utilitarian to Rawlsian Designs for Algorithmic Fairness

TL;DR

This paper introduces a flexible framework for algorithmic fairness that balances utilitarian and Rawlsian principles, allowing for tailored fairness measures based on ethical preferences, with empirical validation on real datasets.

Contribution

It proposes a parameterized class of objective functions interpolating between utilitarian and Rawlsian fairness, analyzing their properties and demonstrating practical tradeoffs.

Findings

Optimal solutions converge to utilitarian and Rawlsian optima

Increasing model complexity improves both fairness measures

Tradeoffs can be tailored to designer preferences

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 the `good' 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 Rawlsian `veil 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 the `good'. This work suggests that the proper degree of `fairness' can be informed by a designer's preferences over the space of induced utilitarian and Rawlsian `good'.