Striking a Balance in Fairness for Dynamic Systems Through Reinforcement
  Learning
Yaowei Hu Jacob Lear Lu Zhang
While significant advancements have been made in the field of fair machine
learning, the majority of studies focus on scenarios where the decision model
operates on a static population. In this paper, we study fairness in dynamic
systems where sequential decisions are made. Each decision may shift the
underlying distribution of features or user behavior. We model the dynamic
system through a Markov Decision Process (MDP). By acknowledging that
traditional fairness notions and long-term fairness are distinct requirements
that may not necessarily align with one another, we propose an algorithmic
framework to integrate various fairness considerations with reinforcement
learning using both pre-processing and in-processing approaches. Three case
studies show that our method can strike a balance between traditional fairness
notions, long-term fairness, and utility.