Title: Fairness with Dynamics,
Abstract: It has recently been shown that if feedback effects of decisions are ignored,
then imposing fairness constraints such as demographic parity or equality of
opportunity can actually exacerbate unfairness. We propose to address this
challenge by modeling feedback effects as the dynamics of a Markov decision
processes (MDPs). First, we define analogs of fairness properties that have
been proposed for supervised learning. Second, we propose algorithms for
learning fair decision-making policies for MDPs. We also explore extensions to
reinforcement learning, where parts of the dynamical system are unknown and
must be learned without violating fairness. Finally, we demonstrate the need to
account for dynamical effects using simulations on a loan applicant MDP.