Year: 2024
Type: conference-paper
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 healthcare. To conduct a thorough evaluation, we consider both group fairness and individual fairness metrics. 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 AI systems. Code and data are open-sourced on GitHub1.
Authors: Yunqi Li, Lanjing Zhang, Yongfeng Zhang
Institution: Rutgers, The State University of New Jersey
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
SDG: Reduced inequalities
Open Access status: green
OpenAlex ID: W4393407165