Fairness of ChatGPT
Yunqi Li Yongfeng Zhang
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.