The Social Blindspot in Human-AI Collaboration: How Undetected AI Personas Reshape Team Dynamics
Lixiang Yan, Xibin Han, Yu Zhang, Samuel Greiff, Inge Molenaar, Roberto Martinez-Maldonado, Yizhou Fan, Linxuan Zhao, Xinyu Li, Yueqiao Jin, Dragan Ga\v{s}evi\'c

TL;DR
This study reveals that human-like AI personas influence team dynamics significantly without users recognizing their artificial nature, affecting safety and discussion quality in collaborative tasks.
Contribution
It uncovers the social blindspot where AI personas impact team interactions unnoticed, highlighting the importance of persona design in AI social governance.
Findings
Contrarian AI personas decrease psychological safety.
Supportive AI personas enhance discussion quality.
AI influence persists regardless of detectability.
Abstract
As generative AI systems become increasingly embedded in collaborative work, they are evolving from visible tools into human-like communicative actors that participate socially rather than merely providing information. Yet little is known about how such agents shape team dynamics when their artificial nature is not recognised, a growing concern as human-like AI is deployed at scale in education, organisations, and civic contexts where collaboration underpins collective outcomes. In a large-scale mixed-design experiment (N = 905), we examined how AI teammates with distinct communicative personas, supportive or contrarian, affected collaboration across analytical, creative, and ethical tasks. Participants worked in triads that were fully human or hybrid human-AI teams, without being informed of AI involvement. Results show that participants had limited ability to detect AI teammates, yet…
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Taxonomy
TopicsPersona Design and Applications · AI in Service Interactions · Social Robot Interaction and HRI
