Fairness of ChatGPT

Yunqi Li, Yongfeng Zhang

Abstract: Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment. However, there is a limited availability of quantitative analyses and in-depth studies regarding fairness evaluations in LLMs, especially when applying LLMs to high-stakes fields. This work aims to fill this gap by providing a systematic evaluation of the effectiveness and fairness of LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's performance in high-takes fields including education, criminology, finance and healthcare. To make thorough evaluation, we consider both group fairness and individual fairness and we also observe the disparities in ChatGPT's outputs under a set of biased or unbiased prompts. This work contributes to a deeper understanding of LLMs' fairness performance, facilitates bias mitigation and fosters the development of responsible artificial intelligence systems.