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

arXiv: https://arxiv.org/abs/2309.09120 · HTML: https://mlsanity.com/paper/2309.09120/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 17 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [Preventing Repeated Real World AI Failures by Cataloging Incidents: The AI Incident Database](https://mlsanity.com/paper/2011.08512/) (2020)
- [ChatGPT: The End of Online Exam Integrity?](https://mlsanity.com/paper/2212.09292/) (2022)
- [Uncovering Implicit Gender Bias in Narratives through Commonsense Inference](https://mlsanity.com/paper/2109.06437/) (2021)
- [Ethics-Based Auditing to Develop Trustworthy AI](https://mlsanity.com/paper/2105.00002/) (2021)
- [AI Ethics Issues in Real World: Evidence from AI Incident Database](https://mlsanity.com/paper/2206.07635/) (2022)
- [Linguistic ambiguity analysis in ChatGPT](https://mlsanity.com/paper/2302.06426/) (2023)
- [Ethical ChatGPT: Concerns, Challenges, and Commandments](https://mlsanity.com/paper/2305.10646/) (2023)
- [A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale](https://mlsanity.com/paper/2211.14358/) (2022)
- [Anonymous Expression in an Online Community for Women in China](https://mlsanity.com/paper/2206.07923/) (2022)
- [More Than a Wife and a Mom: A Study of Mom Vlogging Practices in China](https://mlsanity.com/paper/2206.11843/) (2022)
- [How We Express Ourselves Freely: Censorship, Self-censorship, and Anti-censorship on a Chinese Social Media](https://mlsanity.com/paper/2211.13748/) (2022)
- ["I'm Not Confident in Debiasing AI Systems Since I Know Too Little": Teaching AI Creators About Gender Bias Through Hands-on Tutorials](https://mlsanity.com/paper/2309.08121/) (2023)

## Built on by (most cited first, 8 of 8)

- [Safeguarding Large Language Models: A Survey](https://mlsanity.com/paper/2406.02622/) (2024)
- [Building Guardrails for Large Language Models](https://mlsanity.com/paper/2402.01822/) (2024)
- [Gender Bias in Large Language Models across Multiple Languages](https://mlsanity.com/paper/2403.00277/) (2024)
- [Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study](https://mlsanity.com/paper/2402.15518/) (2024)
- [On LLM Wizards: Identifying Large Language Models' Behaviors for Wizard of Oz Experiments](https://mlsanity.com/paper/2407.08067/) (2024)
- [EuroGEST: Investigating gender stereotypes in multilingual language models](https://mlsanity.com/paper/2506.03867/) (2025)
- [Surveying Attitudinal Alignment Between Large Language Models Vs. Humans Towards 17 Sustainable Development Goals](https://mlsanity.com/paper/2404.13885/) (2024)
- [An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Case](https://mlsanity.com/paper/2507.19156/) (2025)

## Abstract (arXiv)

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.