BAD: BiAs Detection for Large Language Models in the context of candidate screening
This addresses bias and fairness issues in hiring and admissions processes, which is an incremental contribution as it focuses on quantifying existing biases in specific models.
The researchers tackled the problem of social bias in large language models like ChatGPT when used for automated candidate screening, aiming to identify and quantify these biases to show how they could perpetuate inequalities in hiring.
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.