What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement Learning

arXiv:2404.10942v2 Announce Type: replace
Abstract: In sequential decision-making problems involving sensitive attributes like race and gender, reinforcement learning (RL) agents must carefully consider long-term fairness while maximizing returns. Recent works have proposed many different types of fairness notions, but how unfairness arises in RL problems remains unclear. In this paper, we address this gap in the literature by investigating the sources of inequality through a causal lens. We first analyse the causal relationships governing the data generation process and decompose …

arxiv cs.ai cs.cy cs.lg dynamics fairness reinforcement reinforcement learning stat.me type