Fairness of ChatGPT

• URL: http://arxiv.org/abs/2305.18569v2
• Date: Sun, 5 May 2024 19:15:30 GMT
• Title: Fairness of ChatGPT
• Authors: Yunqi Li, Lanjing Zhang, Yongfeng Zhang,
• Abstract summary: This work aims to provide 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. This work contributes to a deeper understanding of LLMs' fairness performance, facilitates bias mitigation and fosters the development of responsible AI systems.
• Score: 30.969927447499405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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