Definitions of Fairness Differ Across Socioeconomic Groups & Shape Perceptions of Algorithmic Decisions

DOI

How algorithmic decisions are perceived by those subject to the decisions is of central importance in a world where they are increasingly utilized. Focusing on San Francisco's decade-long policy of algorithmic school assignments, we draw on procedural and distributive justice theory to investigate parents' fairness perceptions of the assignment algorithm. We find that how parents define fairness, e.g. same rules applied to everyone or receiving their top choice, significantly impacts their overall perception of the fairness of the outcome they received, controlling for the desirability of that outcome. Moreover, people's definitions and perceptions of fairness differ across socioeconomic and racial groups. For instance, among white respondents, the most used definition of fairness was ``proximity'' to their assigned school, whereas, among Hispanic or Latino parents, the most popular definition of fairness was that the ``same rules'' are applied to everyone. It is crucial for computational system designers and policymakers to consider these differences when deciding on the goals and values embedded in decision-making systems and who those goals and values reflect.