Friend, Foe, or Bot? Exploring Intergroup Dynamics in Hybrid Human-Bot Teams
Assem Zhunis, Ziqi Pan, Yuanhao Zhang, Xiaojuan Ma
Abstract
Existing research has examined how artificial teammates influence collaboration within teams, but far less is known about their role in shaping interactions between teams. In particular, it remains unclear how transparent integration of AI teammates influences intergroup biases in competitive contexts. To investigate this, we designed StarHarvest, an online game where two hybrid teams (each consisting of one human and one bot, either concealed or revealed) competed for resources while bots elicited prosocial or antisocial behaviors. Drawing on data from 240 participants, we analyzed behavioral choices, evaluations, and resource allocations toward ingroup and outgroup members. Our findings show that hidden bots fostered stronger within-team coordination but also allowed asymmetric retribution toward weaker opponents. By contrast, revealed bots were treated as secondary teammates, reducing cohesion and shifting responsibility onto human partners. We conclude with design implications for socially responsible integration of artificial teammates, highlighting tensions between group-level and agent-level identities.
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