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This paper investigates fairness in multi-agent LLM systems, moving away from the traditional focus on single, centrally optimized models. They use a hospital triage simulation where two agents, one ethically aligned via RAG and the other unaligned or adversarially prompted, negotiate patient allocation. The key finding is that the joint allocation resulting from negotiation can achieve fairness criteria that neither agent could reach individually, suggesting fairness emerges through interaction.
LLMs can achieve fairer outcomes through multi-agent negotiation than through individual ethical alignment, suggesting a path to fairness via interaction rather than monolithic design.
Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agentic, we propose that fairness emerges through interaction and exchange. We study this via a controlled hospital triage framework in which two agents negotiate over three structured debate rounds. One agent is aligned to a specific ethical framework via retrieval-augmented generation (RAG), while the other is either unaligned or adversarially prompted to favor demographic groups over clinical need. We find that alignment systematically shapes negotiation strategies and allocation patterns, and that neither agent's allocation is ethically adequate in isolation, yet their joint final allocation can satisfy fairness criteria that neither would have reached alone. Aligned agents partially moderate bias through contestation rather than override, acting as corrective patches that restore access for marginalized groups without fully converting a biased counterpart. We further observe that even explicitly aligned agents exhibit intrinsic biases toward certain frameworks, consistent with known left-leaning tendencies in LLMs. We connect these limits to Arrow's Impossibility Theorem: no aggregation mechanism can simultaneously satisfy all desiderata of collective rationality, and multi-agent deliberation navigates rather than resolves this constraint. Our results reposition fairness as an emergent, procedural property of decentralized agent interaction, and the system rather than the individual agent as the appropriate unit of evaluation.
