The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly.
翻译:追求长期公平涉及决策与基本数据生成过程之间的相互作用。在本文中,通过以关于决策分配相互作用的定向循环图(DAG)作为因果模型,我们从动态的角度来调查实现长期公平的可能性。我们提出了在技术上更具挑战性但更自然的概念,在长期、动态的公平分析中可以实现。不同于以前纯粹根据观察到的变量界定的公平概念,我们的概念更进一步了一步,抓住了直接从当前决定影响到未来数据分布的未观察到的潜在因果因素的幕后情况变化。在特定动态下,我们证明总体说来,一个人不可能通过一步的干预实现长期公平目标。此外,在努力争取长期公平的过程中,我们考虑“更接近”长期公平目标的任务,并相应地提出可能性和不可能的结果。</s>