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

arXiv: https://arxiv.org/abs/2604.13103 · HTML: https://mlsanity.com/paper/2604.13103/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 27 of its 65 references that are in the map.
- First posted on arXiv 10 Apr 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [ChatDev: Communicative Agents for Software Development](https://mlsanity.com/paper/2307.07924/) (2023)
- [Bias and Fairness in Large Language Models: A Survey](https://mlsanity.com/paper/2309.00770/) (2023)
- [From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models](https://mlsanity.com/paper/2305.08283/) (2023)
- [Gender bias and stereotypes in Large Language Models](https://mlsanity.com/paper/2308.14921/) (2023)
- [LLM-Based Multi-Agent Systems for Software Engineering: Literature Review, Vision and the Road Ahead](https://mlsanity.com/paper/2404.04834/) (2024)
- [TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems](https://mlsanity.com/paper/2506.04133/) (2025)
- [Bias Testing and Mitigation in LLM-based Code Generation](https://mlsanity.com/paper/2309.14345/) (2023)
- [Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions](https://mlsanity.com/paper/2410.02584/) (2024)
- [AgileCoder: Dynamic Collaborative Agents for Software Development based on Agile Methodology](https://mlsanity.com/paper/2406.11912/) (2024)
- [Agentic Software Engineering: Foundational Pillars and a Research Roadmap](https://mlsanity.com/paper/2509.06216/) (2025)
- [Towards AI-Native Software Engineering (SE 3.0): A Vision and a Challenge Roadmap](https://mlsanity.com/paper/2410.06107/) (2024)
- [Uncovering and Quantifying Social Biases in Code Generation](https://mlsanity.com/paper/2305.15377/) (2023)
- [Bias Unveiled: Investigating Social Bias in LLM-Generated Code](https://mlsanity.com/paper/2411.10351/) (2024)
- [A Survey of LLM-based Automated Program Repair: Taxonomies, Design Paradigms, and Applications](https://mlsanity.com/paper/2506.23749/) (2025)
- [Unmasking Conversational Bias in AI Multiagent Systems](https://mlsanity.com/paper/2501.14844/) (2025)
- [Mitigating Social Bias in Large Language Models: A Multi-Objective Approach within a Multi-Agent Framework](https://mlsanity.com/paper/2412.15504/) (2024)
- [Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models](https://mlsanity.com/paper/2507.05981/) (2025)
- [Facilitating Trustworthy Human-Agent Collaboration in LLM-based Multi-Agent System oriented Software Engineering](https://mlsanity.com/paper/2505.04251/) (2025)
- [An Empirical Study of Group Conformity in Multi-Agent Systems](https://mlsanity.com/paper/2506.01332/) (2025)
- [Can We Trust AI Agents? A Case Study of an LLM-Based Multi-Agent System for Ethical AI](https://mlsanity.com/paper/2411.08881/) (2024)
- [Rapid Reviews in Software Engineering](https://mlsanity.com/paper/2003.10006/) (2020)
- [Multi-Agent Code-Orchestrated Generation for Reliable Infrastructure-as-Code](https://mlsanity.com/paper/2510.03902/) (2025)
- [Analyzing 16,193 LLM Papers for Fun and Profits](https://mlsanity.com/paper/2504.08619/) (2025)
- [Revealing Political Bias in LLMs through Structured Multi-Agent Debate](https://mlsanity.com/paper/2506.11825/) (2025)

## Abstract (arXiv)

Transformer-based large language models (LLMs) and multi-agent systems (MAS) are increasingly embedded across the software development lifecycle (SDLC), yet their fairness implications for developer-facing tools remain underexplored despite their growing role in shaping what code is written, reviewed, and released. We present a rapid review of recent work on fairness in MAS, emphasizing LLM-enabled settings and relevance to software engineering. Starting from an initial set of 350 papers, we screened and filtered the corpus for relevance, retaining 18 studies for final analysis. Across these 18 studies, fairness is framed as a combination of trustworthy AI principles, bias reduction across groups, and interactional dynamics in collectives, while evaluation spans accuracy metrics on bias benchmarks, demographic disparity measures, and emergent MAS-specific notions such as conformity and bias amplification. Reported harms include representational, quality-of-service, security and privacy, and governance failures, which we relate to SDLC stages where evidence is most and least developed. We identify three persistent gaps: (1) fragmented, rarely MAS-specific evaluation practices that limit comparability, (2) limited generalization due to simplified environments and narrow attribute coverage, and (3) scarce, weakly evaluated mitigation and governance mechanisms aligned to real software workflows. These findings suggest MAS fairness research is not yet ready to support deployable, fairness-assured software systems, motivating MAS-aware benchmarks, consistent protocols, and lifecycle-spanning governance.