Collaborative Decision-Making in Human-AI Versus Human-Human Teams: The Influence of Information Inquiry and Team...

ObjectiveWe investigate whether decision-making processes in human-AI teams mirror those in human-only teams in a task where team members hold common and unique information. Specifically, we examine how information inquiry and performance expectations influence these processes.BackgroundHuman-AI decision-making has traditionally been studied as a linear, segmented process where AI provides recommendations and humans make final decisions. However, recent advancements in large language models (LLMs) enable increasingly dynamic interactions similar to the collaborative processes in human-only teams, where members have to share and integrate unique information to reach decisions. Despite the potential for similar dynamics in human-AI teams, research on such configurations-particularly on when unique information is shared-remains limited.MethodWe conducted an experiment using a hidden profile task, a well-established paradigm in team research. We manipulated team type (N_human-AI = 98; N_human-only = 65, three-member teams) and information inquiry, and created conditions with differing performance expectations.ResultsInformation inquiry enhanced information sharing and, consequently, improved decision-making performance, irrespective of performance expectations and team type. Exploratory analyses suggested that while this effect was consistent across team types, information inquiry patterns may differ.ConclusionOur findings prompt avenues for future research on how humans' communication norms vary when interacting with AI versus human team members. Methodologically, this study demonstrates how established decision-making paradigms can be adapted to incorporate AI, offering insights into both human-only and human-AI team processes.ApplicationThe results have practical implications for designing AI systems that promote effective team decision-making by engaging in information inquiry.