一句话：揭示LLM在软件团队组建与任务分配中可能再现偏见，并指出现有研究孤立考察敏感属性的不足。
LLM正被日益用于提升软件工程生产力并支持各类任务，但当其参与团队组建与任务分配等社会敏感决策时，公平性风险随之出现。论文指出，已有研究虽发现LLM可能再现刻板印象，但相关分析仍停留在探索层面，并且往往孤立地考察单一敏感属性，缺乏对LLM驱动软件团队决策中偏见的更系统理解。该文聚焦这一缺口，围绕LLM驱动的软件团队组建与任务分配中的偏见展开研究。摘要未提供具体方法、实验设计与结果；其核心贡献在于提出问题并强调需在团队情境中综合审视敏感属性带来的公平性问题。
LLMs are increasingly used to boost productivity and support software engineering tasks. However, when applied to socially sensitive decisions such as team composition and task allocation, they raise concerns of fairness. Prior studies have revealed that LLMs may reproduce stereotypes; however, these analyses remain exploratory and examine sensitive attributes in isolation.
@inproceedings{paperbot12015,
title = {Once Upon a Team: Investigating Bias in LLM-Driven Software Team Composition and Task Allocation},
author = {Alessandra Parziale and Gianmario Voria and Valeria Pontillo and Amleto Di Salle and Patrizio Pelliccione and Gemma Catolino and Fabio Palomba},
booktitle = {ICSE},
year = {2026},
doi = {10.1145/3786581.3786930}
}