Where are the Humans? A Scoping Review of Fairness in Multi-agent AI Systems

TL;DR

This paper reviews existing research on fairness in multi-agent AI systems, highlighting gaps, limitations, and the need for normative clarity and human oversight.

Contribution

It provides a comprehensive synthesis of 23 studies, identifying five archetypal approaches and emphasizing the importance of embedding fairness throughout development.

Findings

Fairness in MAAI is often superficial and lacks normative foundations.

Explicit human oversight and normative clarity are essential for meaningful fairness evaluation.

The review exposes critical gaps and suggests pathways for future research.

Abstract

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.