Fairness in algorithmic management: Bringing platform-workers into the fold

Abstract

On digital labor platforms, algorithms execute a range of decisions including work assignments, performance evaluation, etc. Although algorithmic decision-making is a key feature of platform work, our understanding of how people perceive decisions made by algorithms -- particularly in terms of the fairness of their processes and outcomes -- remains underdeveloped. The impacts of such perceptions on job satisfaction and perceived organizational support (POS) are also still under exploration with some scholars challenging the possibility of POS among transient platform workers. In this paper, we explored the impacts of the perceived procedural and distributive fairness of algorithms operating in a paradigmatic context of algorithmic management, namely Uber. Drawing on the Theory of Organizational Justice, and a survey of 435 Uber drivers, we not only find that independent platform workers can experience POS, but that the fairness of managerial algorithms (in particular their outcomes) can play a critical role in stimulating such perceptions.