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The pursuit of long-term fairness involves the interplay betweendecision-making and the underlying data generating process. In this paper,through causal modeling with a directed acyclic graph (DAG) on thedecision-distribution interplay, we investigate the possibility of achievinglong-term fairness from a dynamic perspective. We propose Tier Balancing, atechnically more challenging but more natural notion to achieve in the contextof long-term, dynamic fairness analysis. Different from previous fairnessnotions that are defined purely on observed variables, our notion goes one stepfurther, capturing behind-the-scenes situation changes on the unobserved latentcausal factors that directly carry out the influence from the current decisionto the future data distribution. Under the specified dynamics, we prove that ingeneral one cannot achieve the long-term fairness goal only through one-stepinterventions. Furthermore, in the effort of approaching long-term fairness, weconsider the mission of "getting closer to" the long-term fairness goal andpresent possibility and impossibility results accordingly.

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors