Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie

• URL: http://arxiv.org/abs/2309.09120v1
• Date: Sun, 17 Sep 2023 00:53:34 GMT
• Title: Public Perceptions of Gender Bias in Large Language Models: Cases of ChatGPT and Ernie
• Authors: Kyrie Zhixuan Zhou, Madelyn Rose Sanfilippo
• Abstract summary: We conducted a content analysis of social media discussions to gauge public perceptions of gender bias in large language models. People shared both observations of gender bias in their personal use and scientific findings about gender bias in LLMs. We propose governance recommendations to regulate gender bias in LLMs.
• Score: 2.1756081703276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models are quickly gaining momentum, yet are found to demonstrate gender bias in their responses. In this paper, we conducted a content analysis of social media discussions to gauge public perceptions of gender bias in LLMs which are trained in different cultural contexts, i.e., ChatGPT, a US-based LLM, or Ernie, a China-based LLM. People shared both observations of gender bias in their personal use and scientific findings about gender bias in LLMs. A difference between the two LLMs was seen -- ChatGPT was more often found to carry implicit gender bias, e.g., associating men and women with different profession titles, while explicit gender bias was found in Ernie's responses, e.g., overly promoting women's pursuit of marriage over career. Based on the findings, we reflect on the impact of culture on gender bias and propose governance recommendations to regulate gender bias in LLMs.

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