Systemic Fairness
It addresses fairness issues for stakeholders in complex real-world systems like lending, but it is incremental as it builds on existing fairness literature by extending the scope.
The paper tackles the problem of algorithmic fairness in multi-agent ecosystems, arguing that prior work focuses too narrowly on single decision-makers, and it develops formalisms to distinguish firm-level from systemic fairness to advocate for broader ecosystem-wide approaches.
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.