Fairness of ChatGPT

arXiv:2305.18569v2 Announce Type: replace
Abstract: Understanding and addressing unfairness in LLMs are crucial for responsible AI deployment. However, there is a limited number 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 …

abstract ai deployment arxiv chatgpt cs.ai cs.cl cs.cy cs.lg deployment evaluation fairness fields gap however llms quantitative responsible responsible ai studies type understanding work