Bias of AI-generated content: an examination of news produced by large language models
University of Delaware · Tsinghua University · +2 more institutions
Abstract
Large language models (LLMs) have the potential to transform our lives and work through the content they generate, known as AI-Generated Content (AIGC). To harness this transformation, we need to understand the limitations of LLMs. Here, we investigate the bias of AIGC produced by seven representative LLMs, including ChatGPT and LLaMA. We collect news articles from The New York Times and Reuters, both known for their dedication to provide unbiased news. We then apply each examined LLM to generate news content with headlines of these news articles as prompts, and evaluate the gender and racial biases of the AIGC produced by the LLM by comparing the AIGC and the original news articles. We further analyze the…
Citation impact
- FWCI
- 234.87
- Percentile
- 100%
- References
- 28
Authors
6Topics & keywords
- Race (biology)
- Content (measure theory)
- Gender bias
- Content analysis
- Psychology
- Computer science
- Social psychology
- Gender studies
- Gender equality