Application Tracking Systems (ATS) have allowed talent managers, recruiters, and college admissions committees to process large volumes of potential candidate applications efficiently. Traditionally, this screening process was conducted manually, creating major bottlenecks due to the quantity of applications and introducing many instances of human bias. The advent of large language models (LLMs) such as ChatGPT and the potential of adopting methods to current automated application screening raises additional bias and fairness issues that must be addressed. In this project, we wish to identify and quantify the instances of social bias in ChatGPT and other OpenAI LLMs in the context of candidate screening in order to demonstrate how the use of these models could perpetuate existing biases and inequalities in the hiring process.

翻译：申请追踪系统（ATS）使人才管理者、招聘人员及大学招生委员会能够高效处理大量潜在候选人申请。传统上，这一筛选流程依赖人工操作，不仅因申请数量庞大造成显著瓶颈，还引入了诸多人为偏见案例。以ChatGPT为代表的大语言模型（LLMs）的出现，以及将相关方法应用于现有自动化申请筛选系统的可能性，引发了亟需解决的额外偏见与公平性问题。在本项目中，我们旨在识别并量化ChatGPT及其他OpenAI大语言模型在候选人筛选场景中的社会偏见实例，以证明这些模型的应用可能如何延续招聘流程中既有的偏见与不平等现象。