# FAIRGAME: a Framework for AI Agents Bias Recognition using Game Theory

arXiv: https://arxiv.org/abs/2504.14325 · HTML: https://mlsanity.com/paper/2504.14325/

## Key facts

- Cited by 14 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 27 of its 64 references that are in the map.
- First posted on arXiv 19 Apr 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, 27 of 27)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Evaluating Object Hallucination in Large Vision-Language Models](https://mlsanity.com/paper/2305.10355/) (2023)
- [Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study](https://mlsanity.com/paper/2305.13860/) (2023)
- [HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2305.11747/) (2023)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [Nicer Than Humans: How do Large Language Models Behave in the Prisoner's Dilemma?](https://mlsanity.com/paper/2406.13605/) (2024)
- [ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents](https://mlsanity.com/paper/2311.03220/) (2023)
- [Will Systems of LLM Agents Cooperate: An Investigation into a Social Dilemma](https://mlsanity.com/paper/2501.16173/) (2025)
- [When to (or not to) trust intelligent machines: Insights from an evolutionary game theory analysis of trust in repeated games](https://mlsanity.com/paper/2007.11338/) (2020)
- [ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment Analysis](https://mlsanity.com/paper/2402.01715/) (2024)
- [Towards Mechanistic Interpretability of Graph Transformers via Attention Graphs](https://mlsanity.com/paper/2502.12352/) (2025)
- [Personality-Driven Decision-Making in LLM-Based Autonomous Agents](https://mlsanity.com/paper/2504.00727/) (2025)
- [Generative AI for Game Theory-based Mobile Networking](https://mlsanity.com/paper/2404.09699/) (2024)
- [Media and responsible AI governance: a game-theoretic and LLM analysis](https://mlsanity.com/paper/2503.09858/) (2025)
- [Do LLMs trust AI regulation? Emerging behaviour of game-theoretic LLM agents](https://mlsanity.com/paper/2504.08640/) (2025)
- [Language-based game theory in the age of artificial intelligence](https://mlsanity.com/paper/2403.08944/) (2024)
- [Surprising gender biases in GPT](https://mlsanity.com/paper/2407.06003/) (2024)
- [AFSPP: Agent Framework for Shaping Preference and Personality with Large Language Models](https://mlsanity.com/paper/2401.02870/) (2024)
- [Can We Govern the Agent-to-Agent Economy?](https://mlsanity.com/paper/2501.16606/) (2025)
- [Inferring Strategies from Observations in Long Iterated Prisoner's Dilemma Experiments](https://mlsanity.com/paper/2202.04171/) (2022)
- [Entropy-Lens: Uncovering Decision Strategies in LLMs](https://mlsanity.com/paper/2502.16570/) (2025)
- [Large Language Models' Detection of Political Orientation in Newspapers](https://mlsanity.com/paper/2406.00018/) (2024)
- [Utility Fairness in Contextual Dynamic Pricing with Demand Learning](https://mlsanity.com/paper/2311.16528/) (2023)
- [RogueGPT: dis-ethical tuning transforms ChatGPT4 into a Rogue AI in 158 Words](https://mlsanity.com/paper/2407.15009/) (2024)
- [Inducing Personality in LLM-Based Honeypot Agents: Measuring the Effect on Human-Like Agenda Generation](https://mlsanity.com/paper/2503.19752/) (2025)
- [Overcoming the Machine Penalty with Imperfectly Fair AI Agents](https://mlsanity.com/paper/2410.03724/) (2024)

## Built on by (most cited first, 14 of 14)

- [Understanding LLM Agent Behaviours via Game Theory: Strategy Recognition, Biases and Multi-Agent Dynamics](https://mlsanity.com/paper/2512.07462/) (2025)
- [Strategic Communication and Language Bias in Multi-Agent LLM Coordination](https://mlsanity.com/paper/2508.00032/) (2025)
- [Gender Dynamics and Homophily in a Social Network of LLM Agents](https://mlsanity.com/paper/2602.02606/) (2026)
- [Can LLMs effectively provide game-theoretic-based scenarios for cybersecurity?](https://mlsanity.com/paper/2508.05670/) (2025)
- [Competition and Cooperation of LLM Agents in Games](https://mlsanity.com/paper/2604.00487/) (2026)
- [When Numbers Start Talking: Implicit Numerical Coordination Among LLM-Based Agents](https://mlsanity.com/paper/2601.03846/) (2026)
- [Three AI-agents walk into a bar . . . . `Lord of the Flies' tribalism emerges among smart AI-Agents](https://mlsanity.com/paper/2602.23093/) (2026)
- [Coalition Formation in LLM Agent Networks: Stability Analysis and Convergence Guarantees](https://mlsanity.com/paper/2604.14386/) (2026)
- [The AI in the Mirror: LLM Self-Recognition in an Iterated Public Goods Game](https://mlsanity.com/paper/2508.18467/) (2025)
- [Reasonably reasoning AI agents can avoid game-theoretic failures in zero-shot, provably](https://mlsanity.com/paper/2603.18563/) (2026)
- [Cooperative Profiles Predict Multi-Agent LLM Team Performance in AI for Science Workflows](https://mlsanity.com/paper/2604.20658/) (2026)
- [A Multi-Agent Framework for Medical AI: Leveraging Fine-Tuned GPT, LLaMA, and DeepSeek R1 for Evidence-Based and Bias-Aware Clinical Query Processing](https://mlsanity.com/paper/2602.14158/) (2026)
- [Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races](https://mlsanity.com/paper/2608.01193/) (2026)
- [When Numbers Start Talking: Numerical Signalling and Strategic Behaviour Among LLMs](https://mlsanity.com/paper/2610.03033/) (2026)

## Abstract (arXiv)

Letting AI agents interact in multi-agent applications adds a layer of complexity to the interpretability and prediction of AI outcomes, with profound implications for their trustworthy adoption in research and society. Game theory offers powerful models to capture and interpret strategic interaction among agents, but requires the support of reproducible, standardized and user-friendly IT frameworks to enable comparison and interpretation of results. To this end, we present FAIRGAME, a Framework for AI Agents Bias Recognition using Game Theory. We describe its implementation and usage, and we employ it to uncover biased outcomes in popular games among AI agents, depending on the employed Large Language Model (LLM) and used language, as well as on the personality trait or strategic knowledge of the agents. Overall, FAIRGAME allows users to reliably and easily simulate their desired games and scenarios and compare the results across simulation campaigns and with game-theoretic predictions, enabling the systematic discovery of biases, the anticipation of emerging behavior out of strategic interplays, and empowering further research into strategic decision-making using LLM agents.