Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems
For researchers and developers of MAAI systems, this review identifies critical gaps and limitations in current fairness approaches, providing a foundation for more robust fairness integration.
This scoping review synthesizes 23 studies on fairness in Multi-Agent AI (MAAI) systems, finding that fairness is often superficial, lacks normative foundations, and overlooks agent autonomy and system-level interactions. The authors argue for structurally embedding fairness throughout the development lifecycle with explicit human oversight.
Rapid advances in Generative AI are giving rise to increasingly sophisticated Multi-Agent AI (MAAI) systems. While AI fairness has been extensively studied in traditional predictive scenarios, its examination in MAAI remains nascent and fragmented. This scoping review critically synthesizes existing research on fairness in MAAI systems. Through a qualitative content analysis of 23 selected studies, we identify five archetypal approaches. Our findings reveal that fairness in MAAI systems is often addressed superficially, lacks robust normative foundations, and frequently overlooks the complex dynamics introduced by agent autonomy and system-level interactions. We argue that fairness must be embedded structurally throughout the development lifecycle of MAAI, rather than appended as a post-hoc consideration. Meaningful evaluation requires explicit human oversight, normative clarity, and a precise articulation of fairness objectives and beneficiaries. This review provides a foundation for advancing fairness research in MAAI systems by highlighting critical gaps, exposing prevailing limitations, and suggesting pathways.