Payment Algorithm Transparency: A Behavioral Examination (with Basak Kalkanci and Chris Parker)

Abstract: On-demand service platforms have been experimenting with algorithms that determine workers' compensation. While some platforms use commission- or effort-based algorithms that are intuitive to workers, others, in their efforts to better match customer demand, have transitioned to algorithms where pay is not strictly tied to effort, but depends on other, potentially exogenous factors. Platforms have also kept these algorithms opaque. Despite the move towards less-intuitive and opaque algorithms in practice, workers’ reactions to them are not systematically examined or understood. Through incentivized online experiments on Prolific, we offer real-effort tasks as work opportunities for payment to human participants, and examine how features of a pay algorithm, specifically its intuitiveness to workers and transparency, affect workers' engagement (measured by work rejection rates and willingness to pay to accept a work opportunity) and perceptions of the platform. We also examine the effect of an algorithm change from intuitive to non-intuitive, and how transparency interacts with this change.

Background Readings: