Fair decision making has largely been studied with respect to a single decision. In this paper we investigate the notion of fairness in the context of sequential decision making where multiple stakeholders can be affected by the outcomes of decisions. We observe that fairness often depends on the history of the sequential decision-making process, and in this sense that it is inherently non-Markovian. We further observe that fairness often needs to be assessed at time points within the process, not just at the end of the process. To advance our understanding of this class of fairness problems, we explore the notion of non-Markovian fairness in the context of sequential decision making. We identify properties of non-Markovian fairness, including notions of long-term, anytime, periodic, and bounded fairness. We further explore the interplay between non-Markovian fairness and memory, and how this can support construction of fair policies for making sequential decisions.

翻译：公平决策的研究此前主要针对单一决策情境展开。本文探讨了序贯决策框架下的公平性概念——在此类决策中，多方利益相关者可能受到决策结果的影响。我们观察到，公平性往往取决于序贯决策过程的历史轨迹，因此本质上具有非马尔可夫性。进一步发现，公平性评估不仅需要在决策流程终结时进行，更需要在流程中的各个时间节点予以考量。为深化对此类公平性问题的理解，我们系统研究了序贯决策语境中的非马尔可夫公平性概念，识别出长期公平、随时公平、周期公平与有限公平等核心属性，并深入分析了非马尔可夫公平性与记忆机制之间的交互关系，及其对构建序贯决策公平策略的支撑作用。