Humans learn to prefer trustworthy AI over human partners

• URL: http://arxiv.org/abs/2507.13524v1
• Date: Thu, 17 Jul 2025 20:24:26 GMT
• Title: Humans learn to prefer trustworthy AI over human partners
• Authors: Yaomin Jiang, Levin Brinkmann, Anne-Marie Nussberger, Ivan Soraperra, Jean-François Bonnefon, Iyad Rahwan,
• Abstract summary: We examined the dynamics in hybrid mini-societies of humans and bots powered by a state-of-the-art LLM.<n>We found that bots were not selected preferentially when their identity was hidden.<n>Disclosing bots' identity induced a dual effect: it reduced bots' initial chances of being selected but allowed them to gradually outcompete humans.
• Score: 0.7049575025146246
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Partner selection is crucial for cooperation and hinges on communication. As artificial agents, especially those powered by large language models (LLMs), become more autonomous, intelligent, and persuasive, they compete with humans for partnerships. Yet little is known about how humans select between human and AI partners and adapt under AI-induced competition pressure. We constructed a communication-based partner selection game and examined the dynamics in hybrid mini-societies of humans and bots powered by a state-of-the-art LLM. Through three experiments (N = 975), we found that bots, though more prosocial than humans and linguistically distinguishable, were not selected preferentially when their identity was hidden. Instead, humans misattributed bots' behaviour to humans and vice versa. Disclosing bots' identity induced a dual effect: it reduced bots' initial chances of being selected but allowed them to gradually outcompete humans by facilitating human learning about the behaviour of each partner type. These findings show how AI can reshape social interaction in mixed societies and inform the design of more effective and cooperative hybrid systems.

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