# Bias of AI-Generated Content: An Examination of News Produced by Large Language Models

**Authors:** Xiao Fang, Shangkun Che, Minjia Mao, Hongzhe Zhang, Ming Zhao, Xiaohang Zhao  
**arXiv:** [2309.09825](https://arxiv.org/abs/2309.09825)  
**Date:** 2023-09-18  
**Summary page:** https://www.summarizepaper.com/en/paper/2309.09825v1/

## Key points

- Study titled "Bias of AI-Generated Content" by Xiao Fang et al. explores impact of Large Language Models (LLMs) on our lives and work through AI-Generated Content (AIGC)
- Emphasizes importance of understanding limitations of LLMs to effectively harness transformation
- Focuses on investigating bias in AIGC produced by seven representative LLMs, including ChatGPT and LLaMA
- Analysis conducted using news articles from reputable sources like The New York Times and Reuters as prompts for LLMs
- Findings reveal significant gender and racial biases in AIGC generated by examined LLMs, with discrimination against females and individuals of Black race
- ChatGPT stands out for demonstrating lower bias level compared to other models, able to decline content generation when presented with biased prompts
- Research highlights critical need to address bias in AI-generated content and develop strategies to mitigate discriminatory outcomes for more equitable and inclusive AI technologies

## Summary

In their study titled "Bias of AI-Generated Content: An Examination of News Produced by Large Language Models," authors Xiao Fang, Shangkun Che, Minjia Mao, Hongzhe Zhang, Ming Zhao, and Xiaohang Zhao delve into the potential transformative impact of Large Language Models (LLMs) on our lives and work through the generation of AI-Generated Content (AIGC). The researchers emphasize the importance of understanding the limitations of LLMs to effectively harness this transformation. The study focuses on investigating the bias present in AIGC produced by seven representative LLMs, with a specific emphasis on models like ChatGPT and LLaMA. To conduct their analysis, the authors collect news articles from reputable sources such as The New York Times and Reuters, known for delivering relatively unbiased news content. These articles serve as prompts for the LLMs to generate news content. The findings reveal significant gender and racial biases in the AIGC generated by each examined LLM. Particularly concerning is the discrimination against females and individuals of Black race evident in the content produced by these models. Amongst the LLMs studied, ChatGPT stands out for demonstrating a lower level of bias compared to others. Notably, ChatGPT showcases an ability to decline content generation when presented with biased prompts. Overall, this research sheds light on the critical need to address bias in AI-generated content and underscores the importance of developing strategies to mitigate discriminatory outcomes in language model outputs. By understanding and addressing these biases, we can strive towards more equitable and inclusive AI technologies that better serve diverse populations.