Long-Term Fairness with Unknown Dynamics

Abstract

As populations adapt to algorithmic prediction, machine learning can myopically reinforce social inequalities or dynamically seek equitable outcomes. In this paper, we formalize prediction subject to long-term fairness as a constrained online reinforcement learning problem. This formulation can accommodate dynamical control objectives, such as inducing equitable population adaptations, that cannot be expressed by static formulations of fairness. By adapting recent work in online learning, we provide the first algorithm that guarantees simultaneous, probabilistic bounds on cumulative loss and cumulative violations of fairness (defined as statistical regularities between demographic groups) in this setting. We compare this algorithm to an off-the-shelf, deep reinforcement learning algorithm that lacks such safety guarantees, and to a repeatedly retrained, myopic classifier, as a baseline. We demonstrate that a reinforcement learning framework for long-term fairness allows algorithms to adapt to unknown dynamics and sacrifice short-term profit or fairness to drive a classifier-population system towards more desirable equilibria. Our experiments model human populations according to evolutionary game theory, using real-world data to set an initial state.