Fairness in Agentic AI: A Unified Framework for Ethical and Equitable Multi-Agent System

TL;DR

This paper introduces a comprehensive framework for ensuring fairness in multi-agent AI systems by treating fairness as an emergent property, integrating constraints, bias mitigation, and incentives to promote ethical and equitable decision-making.

Contribution

It presents a novel, unified framework that models fairness as an emergent property in multi-agent systems, bridging AI ethics with system design.

Findings

Fairness constraints improve decision equity

Incentive mechanisms align agent behaviors with societal values

Empirical validation shows enhanced fairness in multi-agent interactions

Abstract

Ensuring fairness in decentralized multi-agent systems presents significant challenges due to emergent biases, systemic inefficiencies, and conflicting agent incentives. This paper provides a comprehensive survey of fairness in multi-agent AI, introducing a novel framework where fairness is treated as a dynamic, emergent property of agent interactions. The framework integrates fairness constraints, bias mitigation strategies, and incentive mechanisms to align autonomous agent behaviors with societal values while balancing efficiency and robustness. Through empirical validation, we demonstrate that incorporating fairness constraints results in more equitable decision-making. This work bridges the gap between AI ethics and system design, offering a foundation for accountable, transparent, and socially responsible multi-agent AI systems.