Online freelance marketplaces, a rapidly growing part of the global labor market, are creating a fair environment where professional skills are the main factor for hiring. While these platforms can reduce bias from traditional hiring, the personal information in user profiles raises concerns about ongoing discrimination. Past studies on this topic have mostly used existing data, which makes it hard to control for other factors and clearly see the effect of things like gender or race. To solve these problems, this paper presents a new method that uses Retrieval-Augmented Generation (RAG) with a Large Language Model (LLM) to create realistic, artificial freelancer profiles for controlled experiments. This approach effectively separates individual factors, enabling a clearer statistical analysis of how different variables influence the freelancer project process. In addition to analyzing extracted data with traditional statistical methods for post-project stage analysis, our research utilizes a dataset with highly controlled variables, generated by an RAG-LLM, to conduct a simulated hiring experiment for pre-project stage analysis. The results of our experiments show that, regarding gender, while no significant preference emerged in initial hiring decisions, female freelancers are substantially more likely to receive imperfect ratings post-project stage. Regarding regional bias, a strong and consistent preference favoring US-based freelancers shows that people are more likely to be selected in the simulated experiments, perceived as more leader-like, and receive higher ratings on the live platform.

翻译：在线自由职业市场作为全球劳动力市场中快速增长的部分，正致力于打造以专业技能为核心雇佣标准的公平环境。尽管这些平台能够减少传统招聘中的偏见，但用户档案中的个人信息引发了人们对持续存在的歧视现象的担忧。以往关于此主题的研究大多使用现有数据，这难以控制其他混杂因素，也无法清晰揭示性别或种族等因素的影响。为解决这些问题，本文提出一种新方法，利用检索增强生成技术与大型语言模型创建逼真的模拟自由职业者档案，以进行受控实验。该方法能有效分离个体变量，从而更清晰地统计分析不同因素如何影响自由职业者项目流程。除了使用传统统计方法对提取数据进行项目后阶段分析外，本研究还采用由RAG-LLM生成的高度受控变量数据集，开展模拟招聘实验以进行项目前阶段分析。实验结果表明：在性别方面，虽然初始雇佣决策未出现显著偏好，但女性自由职业者在项目后阶段获得非完美评价的概率显著更高；在地域偏见方面，对美国本土自由职业者存在强烈且一致的偏好，表现为在模拟实验中更易被选中、被认为更具领导力特质，并在实际平台中获得更高评分。