BAD: BiAs Detection for Large Language Models in the context of candidate screening

TL;DR

This paper investigates social biases in large language models like ChatGPT used for candidate screening, highlighting how these biases can perpetuate inequalities in hiring processes.

Contribution

It introduces a method to detect and quantify social biases in LLMs specifically in the context of candidate screening applications.

Findings

ChatGPT exhibits measurable social biases in candidate screening scenarios.

Biases identified could influence hiring decisions and reinforce societal inequalities.

The study provides a framework for bias detection in LLMs used in recruitment.

Abstract

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.