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, and where decision making may be informed by additional
constraints and criteria beyond the requirement of fairness. In this setting,
we observe that fairness often depends on the history of the sequential
decision-making process and not just on the current state. To advance our
understanding of this class of fairness problems, we define 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 in sequential decision-making settings.

非马尔科夫公平关系在顺序决策制定中起着重要作用，研究涉及多方利益相关者和附加约束条件的决策制定过程中的公平性问题。

记得公平：关于序列决策中的非马尔可夫公平（初步报告）

Remembering to Be Fair: On Non-Markovian Fairness in Sequential  DecisionMaking (Preliminary Report)

When an AI system interacts with multiple users, it frequently needs to make
allocation decisions. For instance, a virtual agent decides whom to pay
attention to in a group setting, or a factory robot selects a worker to deliver
a part. Demonstrating fairness in decision making is essential for such systems
to be broadly accepted. We introduce a Multi-Armed Bandit algorithm with
fairness constraints, where fairness is defined as a minimum rate that a task
or a resource is assigned to a user. The proposed algorithm uses contextual
information about the users and the task and makes no assumptions on how the
losses capturing the performance of different users are generated. We provide
theoretical guarantees of performance and empirical results from simulation and
an online user study. The results highlight the benefit of accounting for
contexts in fair decision making, especially when users perform better at some
contexts and worse at others.

本研究介绍一种带有公平性约束条件的多臂赌博算法，使用关于用户和任务的上下文信息进行决策制定，以展示公平的决策结果