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：机器学习算法日益广泛地用于制定或辅助各类决策。随着这种广泛应用，人们对此类方法的公平性也愈发担忧。先前关于算法公平性的文献已广泛探讨了相关风险，并在许多情况下提出了应对其中部分风险的方法。然而，大多数研究聚焦于由（单一）焦点决策者或代理个体行为所引发的公平性问题。相比之下，现实世界的多数系统包含众多协作行动的代理，它们共同构成更大的生态系统。例如，在贷款场景中，存在多位评估申请人贷款的放贷方，以及其决策同样影响结果的政策制定者和其他机构。因此，单个决策者的任何放贷决策所产生的更广泛影响，很可能取决于生态系统中多个不同代理的行为。本文为非系统性公平与系统性公平建立了形式化定义，并呼吁算法公平性研究在现实情境中更加关注生态系统层面的公平——或简称为系统性公平。