A Just Approach Balancing Rawlsian Leximax Fairness and Utilitarianism

Violet (Xinying) Chen, J.N. Hooker 2020 AAAI/ACM Conference on AI, Ethics, and Society (AIES ’20) February 2020

Download paper here

Keywords: balancing fairness and efficiency, distributive justice

This paper studies the challenging task of defining and modeling a proper fairness-efficiency trade off. We define fairness with Rawlsian leximax fairness, which views the lexicographic maximum among all feasible outcomes as the most equitable; and define efficiency with Utilitarianism, which seeks to maximize the sum of utilities received by entities regardless of individual differences. Motivated by a justice-driven trade off principle: prioritize fairness to benefit the less advantaged unless too much efficiency is sacrificed, we propose a sequential optimization procedure to balance leximax fairness and utilitarianism: each iteration maximizes a social welfare function, and we provide a practical mixed integer/linear programming (MILP) formulation for each maximization problem.