Human-Algorithm Collaboration in Gig Work: The Role of Experience, Skill Level, and Task Complexity

Abstract

In this paper, we contribute to recent studies on human-algorithm collaboration by examining how experience, skill level, workload, and task complexity shape the impact of an algorithm-enabled decision-support tool for gig workers. We leverage a large-scale randomized field experiment on the Instacart platform from June 2022 to September 2022. The algorithm-enabled technology aims to revolutionize item picking by helping shoppers locate and collect items more efficiently, reducing picking time while maintaining service quality, as reflected by refund rates. We find that the technology complements experience: rather than diminishing the value of experience, it yields larger improvements for more experienced shoppers. We also find that it substitutes for skill levels by helping lower-skilled workers bridge the performance gap with higher-skilled peers, but lower-skilled workers need experience to fully benefit from the tool. Finally, treatment effects vary with workload and task complexity, clarifying when algorithmic guidance is most valuable. For policymakers, our findings suggest a simple rule: give workers some baseline experience before introducing AI tools, using a staggered rollout with basic training. We also show that these tools can make service more consistent by closing the gap between high performers and lower performers, reducing performance dispersion, and helping standardize quality.