Fairness in Multi-Agent AI: A Unified Framework for Ethical and Equitable Autonomous Systems

Rajesh Ranjan* (Carnegie Mellon University, USA) Shailja Gupta* (Carnegie Mellon University, USA) Surya Narayan Singh* (BIT Sindri, India) 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. Introduction As artificial intelligence (AI) systems evolve, Agentic AI --autonomous systems capable of independent decision-making and goal-setting--has emerged as a ...