Long-Term Fairness with Unknown Dynamics

As machine learning (ML) algorithms are deployed for tasks with real-world social consequences (e.g., school admissions, loan approval, medical interventions, etc.), the possibility exists for runaway social inequalities (Crawford and Calo, 2016; Chaney et al., 2018; Fuster et al., 2018; Ensign et al., 2018). While "fairness" has become a salient ethical concern in contemporary research, the closed-loop dynamics of real-world systems comprising ML policies and populations that mutually adapt to each other (Figure 1 in the supplementary material) remain poorly understood. In this paper, our primary contribution is to consider the problem of long-term fairness, or algorithmic fairness in the context of a dynamically responsive population, as a reinforcement learning (RL) problem subject to constraint. In our formulation, the central learning task is to develop a policy that minimizes cumulative loss (e.g., financial risk, negative educational outcomes, misdiagnoses, etc.) incurred by an ML agent interacting with a human population up to a finite time horizon, subject to constraints on cumulative "violations of fairness", which we refer to in a single time step as disparity and cumulatively as distortion.