The Coin of AI Has Two Sides: Matching Enhancement and Information Revelation Effects of AI on Gig-Economy Platforms

Abstract

Artificial intelligence (AI) has been increasingly integrated into the process of matching between workers and employers requesting job tasks on a gig-economy platform. Unlike the conventional wisdom that adopting AI in the matching process always benefits the platform by assigning better-matched jobs (employers) to workers, we discover unintended but possible revenue-decreasing consequences for the AI-adopting platform. We build a stylized game theoretical model that considers gig workers’ strategic participation behavior. We find that while the matching enhancement effect of AI can increase the platform’s revenue by improving matching quality, AI-assigned jobs can also reveal information about the uncertain labor demand to workers and thus unfavorably change workers’ participation decisions, resulting in revenue loss for the platform. We extend our model to the cases where (1) the share of revenue between workers and platform is endogenous and (2) the workers compete for the job tasks, and find consistent results. Furthermore, we examine two approaches to mitigate the potential negative effect of AI-enabled matching for the platform and find that under certain conditions, the AI-adopting platform can be better off by revealing the labor demand or competition information directly to workers. Our results shed light on both the intended positive and unintended negative roles of utilizing AI to facilitate matching, and highlight the importance of thoughtful development, management, and application of AI in the gig economy.