How to build fair multi-agent AI systems?
Fairness in Multi-Agent AI: A Unified Framework for Ethical and Equitable Autonomous Systems
February 12, 2025
https://arxiv.org/pdf/2502.07254This paper explores fairness in decentralized multi-agent AI systems, where biases can emerge from agent interactions rather than just individual agents or training data. It proposes a framework for addressing this, treating fairness as a dynamic system property.
Key points for LLM-based multi-agent systems:
- Emergent Bias: LLMs interacting in a multi-agent system can develop and amplify biases through their interactions, even if individual LLMs are designed to be unbiased.
- Dynamic Fairness: Fairness needs to be addressed as an ongoing, evolving property of the system, not a static constraint. The proposed framework uses fairness constraints, bias correction mechanisms, and incentives to encourage fair behavior from LLMs in real-time.
- Resource Allocation: Competition for resources (compute, data, etc.) in multi-agent LLM systems needs fairness considerations to prevent one LLM or group of LLMs from dominating.
- Explainability and Governance: Transparency in decision-making is critical for multi-agent LLM systems. Explainable AI (XAI) methods can help understand why LLMs take specific actions, especially when those actions involve fairness trade-offs. Robust governance frameworks are needed to monitor, intervene, and ensure fairness is maintained.
- Adversarial Exploits: Malicious LLMs could exploit fairness mechanisms to gain advantages, requiring the development of robust and adaptable fairness models.