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

arXiv: https://arxiv.org/abs/2309.09825 · HTML: https://mlsanity.com/paper/2309.09825/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 43 references that are in the map.
- First posted on arXiv 18 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [Defending Against Neural Fake News](https://mlsanity.com/paper/1905.12616/) (2019)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [Towards Understanding and Mitigating Social Biases in Language Models](https://mlsanity.com/paper/2106.13219/) (2021)
- [Mitigating Gender Bias in Natural Language Processing: Literature Review](https://mlsanity.com/paper/1906.08976/) (2019)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Reducing Sentiment Bias in Language Models via Counterfactual Evaluation](https://mlsanity.com/paper/1911.03064/) (2019)
- [Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models](https://mlsanity.com/paper/2102.04130/) (2021)
- [Contrasting Linguistic Patterns in Human and LLM-Generated News Text](https://mlsanity.com/paper/2308.09067/) (2023)
- [Mitigating Gender Bias in Machine Learning Data Sets](https://mlsanity.com/paper/2005.06898/) (2020)

## Built on by (most cited first, 30 of 38)

- [A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly](https://mlsanity.com/paper/2312.02003/) (2023)
- [Training-free LLM-generated Text Detection by Mining Token Probability Sequences](https://mlsanity.com/paper/2410.06072/) (2024)
- [On the Challenges and Opportunities in Generative AI](https://mlsanity.com/paper/2403.00025/) (2024)
- [Navigating LLM Ethics: Advancements, Challenges, and Future Directions](https://mlsanity.com/paper/2406.18841/) (2024)
- [Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models](https://mlsanity.com/paper/2407.13934/) (2024)
- [Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement](https://mlsanity.com/paper/2410.14259/) (2024)
- [MetaIE: Distilling a Meta Model from LLM for All Kinds of Information Extraction Tasks](https://mlsanity.com/paper/2404.00457/) (2024)
- [Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities](https://mlsanity.com/paper/2310.19626/) (2023)
- [Persona Setting Pitfall: Persistent Outgroup Biases in Large Language Models Arising from Social Identity Adoption](https://mlsanity.com/paper/2409.03843/) (2024)
- [Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM](https://mlsanity.com/paper/2508.05775/) (2025)
- [Learn-to-Distance: Distance Learning for Detecting LLM-Generated Text](https://mlsanity.com/paper/2601.21895/) (2026)
- [Beyond principlism: Practical strategies for ethical AI use in research practices](https://mlsanity.com/paper/2401.15284/) (2024)
- [How Reliable AI Chatbots are for Disease Prediction from Patient Complaints?](https://mlsanity.com/paper/2405.13219/) (2024)
- ["It Might be Technically Impressive, But It's Practically Useless to us": Motivations, Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry](https://mlsanity.com/paper/2409.12000/) (2024)
- [Programming with AI: Evaluating ChatGPT, Gemini, AlphaCode, and GitHub Copilot for Programmers](https://mlsanity.com/paper/2411.09224/) (2024)
- [Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators](https://mlsanity.com/paper/2502.09799/) (2025)
- [Position: We Need An Algorithmic Understanding of Generative AI](https://mlsanity.com/paper/2507.07544/) (2025)
- [A Multi-Task Evaluation of LLMs' Processing of Academic Text Input](https://mlsanity.com/paper/2508.11779/) (2025)
- [Trustworthy AI-Generative Content for Intelligent Network Service: Robustness, Security, and Fairness](https://mlsanity.com/paper/2405.05930/) (2024)
- [Toxicity in Online Platforms and AI Systems: A Survey of Needs, Challenges, Mitigations, and Future Directions](https://mlsanity.com/paper/2509.25539/) (2025)
- [Generating the Modal Worker: A Cross-Model Audit of Race and Gender in LLM-Generated Personas Across 41 Occupations](https://mlsanity.com/paper/2510.21011/) (2025)
- [Mapping the Media Landscape: Predicting Factual Reporting and Political Bias Through Web Interactions](https://mlsanity.com/paper/2410.17655/) (2024)
- [Evaluating Gender Bias in Large Language Models](https://mlsanity.com/paper/2411.09826/) (2024)
- [Short-PHD: Detecting Short LLM-generated Text with Topological Data Analysis After Off-topic Content Insertion](https://mlsanity.com/paper/2504.02873/) (2025)
- [Do LLMs have a Gender (Entropy) Bias?](https://mlsanity.com/paper/2505.20343/) (2025)
- [Regional Bias in Large Language Models](https://mlsanity.com/paper/2601.16349/) (2026)
- [Enhancing Journalism with AI: A Study of Contextualized Image Captioning for News Articles using LLMs and LMMs](https://mlsanity.com/paper/2408.04331/) (2024)
- [Assessing instructor-AI cooperation for grading essay-type questions in an introductory sociology course](https://mlsanity.com/paper/2501.06461/) (2025)
- [Measuring Stereotype and Deviation Biases in Large Language Models](https://mlsanity.com/paper/2508.06649/) (2025)
- [Perceived Political Bias in LLMs Reduces Persuasive Abilities](https://mlsanity.com/paper/2602.18092/) (2026)

## Abstract (arXiv)

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 gender bias of each LLM under biased prompts by adding gender-biased messages to prompts constructed from these news headlines. Our study reveals that the AIGC produced by each examined LLM demonstrates substantial gender and racial biases. Moreover, the AIGC generated by each LLM exhibits notable discrimination against females and individuals of the Black race. Among the LLMs, the AIGC generated by ChatGPT demonstrates the lowest level of bias, and ChatGPT is the sole model capable of declining content generation when provided with biased prompts.