Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration

TL;DR

This paper explores how fairness emerges from multi-agent interactions in language models, showing that negotiation and alignment influence fairness outcomes and that collective deliberation navigates inherent impossibility constraints.

Contribution

It introduces a framework for studying fairness as an emergent property of agent interactions, highlighting the role of negotiation and alignment in fairness outcomes.

Findings

Aligned agents moderate bias through contestation rather than override

Joint allocations can satisfy fairness criteria not achievable individually

Intrinsic biases persist even in explicitly aligned agents

Abstract

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.