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

arXiv cs.AI / 4/17/2026

💬 OpinionIdeas & Deep AnalysisModels & Research

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Key Points

A scoping review of 23 studies finds that fairness research in multi-agent AI (MAAI) is still fragmented and relatively underdeveloped compared with traditional predictive AI scenarios.
The review identifies five common “archetypal” approaches to fairness in MAAI, but concludes that many treatments remain superficial and lack solid normative (value/standard) foundations.
It highlights that agent autonomy and system-level interactions create complex dynamics that are often ignored, undermining the way fairness is defined and assessed.
The authors argue fairness should be built into the MAAI development lifecycle (structurally, not as an afterthought), with meaningful evaluation requiring clear fairness goals and human oversight.
The paper aims to advance the field by outlining key gaps, exposing recurring limitations, and proposing directions for future fairness research in MAAI.

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.