BAD: BiAs Detection for Large Language Models in the context of
  candidate screening
Nam Ho Koh Joseph Plata Joyce Chai
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.