# Interactional Fairness in LLM Multi-Agent Systems: An Evaluation Framework

arXiv: https://arxiv.org/abs/2505.12001 · HTML: https://mlsanity.com/paper/2505.12001/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 17 May 2025.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 7 of 7)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Social Simulacra: Creating Populated Prototypes for Social Computing Systems](https://mlsanity.com/paper/2208.04024/) (2022)
- [Learning Fairness in Multi-Agent Systems](https://mlsanity.com/paper/1910.14472/) (2019)
- [Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance](https://mlsanity.com/paper/2402.14531/) (2024)
- [Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2012.09421/) (2020)
- [Are LLMs Socially Adaptive? Contrasting Belief Evolution in Large Language Models and Humans](https://mlsanity.com/paper/2410.10398/) (2024)

## Built on by (most cited first, 6 of 6)

- [Traccia: An OpenTelemetry-Based Governance Platform for AI Systems](https://mlsanity.com/paper/2607.14309/) (2026)
- [Beyond Arrow's Impossibility: Fairness as an Emergent Property of Multi-Agent Collaboration](https://mlsanity.com/paper/2604.13705/) (2026)
- [Fair Agents: Balancing Multistakeholder Alignment in Multi-Agent Personalization Systems](https://mlsanity.com/paper/2605.02379/) (2026)
- [In-Situ Behavioral Evaluation for LLM Fairness, Not Standardized-Test Scores](https://mlsanity.com/paper/2605.12530/) (2026)
- [Beyond Outcome Gaps: Process-Aware Fairness Diagnosis for LLM-based Multi-Agent Decision Systems](https://mlsanity.com/paper/2609.02092/) (2026)
- [When Agents Act Unwatched: The Reduced-Supervision Paradox in Agentic AI](https://mlsanity.com/paper/2609.29547/) (2026)

## Abstract (arXiv)

As large language models (LLMs) are increasingly used in multi-agent systems, questions of fairness should extend beyond resource distribution and procedural design to include the fairness of how agents communicate. Drawing from organizational psychology, we introduce a novel framework for evaluating Interactional fairness encompassing Interpersonal fairness (IF) and Informational fairness (InfF) in LLM-based multi-agent systems (LLM-MAS). We extend the theoretical grounding of Interactional Fairness to non-sentient agents, reframing fairness as a socially interpretable signal rather than a subjective experience. We then adapt established tools from organizational justice research, including Colquitt's Organizational Justice Scale and the Critical Incident Technique, to measure fairness as a behavioral property of agent interaction. We validate our framework through a pilot study using controlled simulations of a resource negotiation task. We systematically manipulate tone, explanation quality, outcome inequality, and task framing (collaborative vs. competitive) to assess how IF influences agent behavior. Results show that tone and justification quality significantly affect acceptance decisions even when objective outcomes are held constant. In addition, the influence of IF vs. InfF varies with context. This work lays the foundation for fairness auditing and norm-sensitive alignment in LLM-MAS.