Machine learning algorithms are increasingly used to make or support decisions in a wide range of settings. With such expansive use there is also growing concern about the fairness of such methods. Prior literature on algorithmic fairness has extensively addressed risks and in many cases presented approaches to manage some of them. However, most studies have focused on fairness issues that arise from actions taken by a (single) focal decision-maker or agent. In contrast, most real-world systems have many agents that work collectively as part of a larger ecosystem. For example, in a lending scenario, there are multiple lenders who evaluate loans for applicants, along with policymakers and other institutions whose decisions also affect outcomes. Thus, the broader impact of any lending decision of a single decision maker will likely depend on the actions of multiple different agents in the ecosystem. This paper develops formalisms for firm versus systemic fairness, and calls for a greater focus in the algorithmic fairness literature on ecosystem-wide fairness - or more simply systemic fairness - in real-world contexts.

翻译：机器学习算法越来越广泛地用于在各种环境中做出或支持决策。因为使用范围变得如此广泛，所以人们也越来越关心这些方法的公正性。先前有关算法公正性的文献广泛探讨了风险，并在许多情况下提出了管理其中一些风险的方法。然而，大多数研究都专注于由（单个）焦点决策者或代理人采取的公正性问题。相比之下，大多数现实世界的系统都由许多代理人共同作为较大生态系统的一部分协作工作。例如，在贷款情况下，有多个贷方评估申请人的贷款，以及政策制定者和其他机构的决策也会影响结果。因此，单个决策者作出任何放贷决定的更广泛影响可能取决于生态系统中多个不同代理人的行动。本文开发了牢固对系统性公正性的公式，并呼吁算法公正性文献更加关注现实环境中的系统性公正性。