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

2026-04-16

AI Summary

Research on fairness in current multi-agent artificial intelligence systems remains nascent and fragmented, often overlooking the human role, normative foundations, and system dynamics. Through a scoping review and qualitative content analysis of 23 relevant studies, this work systematically synthesizes existing approaches and identifies five archetypal fairness strategies for the first time. It further uncovers critical gaps in the literature, particularly the absence of human oversight and ambiguity in normative grounding. Building on these insights, the paper proposes a novel paradigm that structurally embeds fairness throughout the entire system lifecycle, offering a unified theoretical framework and a clear pathway for future advancements in the field.

Technology Category

• Multiagent Systems: Agent/AI Theories and Architectures
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Game Theory and Economic Paradigms: Fair Division

Application Category

• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• fairness
• multi-agent AI systems
• human oversight
• normative foundations
• agent autonomy

Innovation

• Multi-Agent AI
• Fairness
• Scoping Review
• Normative Foundations
• Human Oversight