The Social Cost of Intelligence: Emergence, Propagation, and Amplification of Stereotypical Bias in Multi-Agent Systems
arXiv:2510.10943v2 Announce Type: replace-cross Abstract: Bias in large language models (LLMs) remains a persistent challenge, often leading to stereotyping and unfair treatment across social groups. While prior work has mainly focused on individual LLMs, the emergence of multi-agent systems (MAS), where multiple LLMs collaborate and communicate, introduces new and underexplored dynamics in how bias emerges, propagates, and amplifies. To systematically investigate these dynamics, we propose a simple evaluation framework with three agent-level metrics that quantify bias emergence, propagation,
The proliferation of multi-agent systems built upon LLMs necessitates a deeper understanding of emergent biases, moving beyond individual model limitations as these systems become more prevalent.
As AI systems grow in complexity and autonomy, the mechanisms by which biases interact and amplify within multi-agent environments become critical for fair and effective deployment.
The focus of AI bias research shifts from purely individual LLM issues to the systemic properties of interacting AI agents, requiring new evaluation frameworks and mitigation strategies.
- · AI ethics researchers
- · AI system developers
- · Regulatory bodies
- · Companies deploying unexamined MAS
- · Social groups impacted by amplified bias
Companies will need to invest more in testing and mitigating emergent biases in their multi-agent AI systems.
New standards and regulations specifically addressing bias propagation in multi-agent AI could emerge, impacting development cycles and market access.
Public distrust in autonomous AI systems could increase if emergent biases lead to significant real-world harm, potentially slowing adoption or leading to calls for stricter human oversight.
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Read at arXiv cs.CL