Exploring the Sources of Inequality in Reinforcement Learning through a Causal Lens

Inequality in reinforcement learning can stem from various sources, including the environmental dynamics, decision-making, and historical disparities. By decomposing the causal effect of sensitive attributes on long-term well-being, this work introduces a novel fairness notion called dynamics fairness to capture the fairness of the underlying mechanisms governing the environment.