Presentation
Leader-Follower Hierarchical Role Effects on Individual and Shared Trust in Mixed Human-AI Teams
DescriptionWith the rapid advancement of autonomous technologies, the role of AI in supporting human work is expanding beyond simple decision aids to increasingly autonomous task support, leading to growing interest in diverse forms of human-AI teams (HATs). As these team configurations evolve toward more complex multi-human multi-agent structures, it becomes increasingly important to understand key team characteristics such as leadership structure. Conventionally, humans have primarily taken leadership positions, directing and supervising autonomous agents. However, emerging HAT contexts now enable humans to assume a wider range of hierarchical roles within teams, including both leader and follower positions. Despite these developments, empirical research on non-dyadic HATs remains limited, leaving important questions regarding trust and teamwork in complex team structures largely unexplored. In this study, we investigate how hierarchical roles differentially shape trust in AI teammates and trust in the team as a whole, as well as how role-based divergence in trust associates with overall team outcomes. Our findings reveal that trust varies significantly by the trustor's role, while shared trust and reduced trust divergence across teammates are critical for achieving better team performance. Accordingly, it is important to develop mechanisms to support shared team cognition and mitigate trust divergence.
Event Type
Lecture
TimeWednesday, October 21st2:10pm - 2:30pm PDT
Location
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