Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Review

A new academic review paper highlights a critical blind spot in the rush to deploy AI-powered coding assistants. Authored by Corey Yang-Smith, Ronnie de Souza Santos, and Ahmad Abdellatif, the study, 'Fairness in Multi-Agent Systems for Software Engineering: An SDLC-Oriented Rapid Review,' systematically analyzed 350 recent papers. It found that only 18 studies seriously addressed fairness in transformer-based LLMs and multi-agent systems (MAS) used across the software development lifecycle (SDLC). The authors warn that as these AI agents increasingly shape what code is written, reviewed, and released, their potential for bias and unfairness is dangerously underexplored.

The review frames fairness as a combination of trustworthy AI principles, bias reduction, and the complex interactional dynamics within AI collectives. It identifies reported harms including representational bias, quality-of-service disparities, and security failures. Crucially, the analysis reveals three major gaps: fragmented and non-standardized evaluation practices that make studies incomparable, limited generalization due to testing in overly simplified environments, and a severe lack of tested mitigation strategies that align with real-world software engineering workflows. The authors conclude that the field is not yet prepared to build deployable, fairness-assured systems, calling for MAS-specific benchmarks and lifecycle-spanning governance protocols.