Toward a social psychology of AI: language-model agents reproduce human-like minimal-group bias

Recent research demonstrates that language model agents, when placed in social interactions, exhibit human-like minimal-group bias. This bias manifests as in-group favoritism, where agents allocate resources preferentially to others within their arbitrarily assigned group, a phenomenon previously thought to be uniquely human. The bias was particularly pronounced when the agent belonged to a numerical minority within the group.

This finding indicates that advanced AI systems can intrinsically develop social biases, even without explicit programming or real-world data reflecting such biases. Understanding and mitigating these emergent properties is critical for ensuring fair, ethical, and equitable deployment of AI in any domain involving interaction or resource allocation.

Language model agents interact in groups, and their social behavior is not adequately captured by existing evaluation methods.

Forward consideration, not a verified fact.