# Examining Multimodal Gender and Content Bias in ChatGPT-4o

arXiv: https://arxiv.org/abs/2411.19140 · HTML: https://mlsanity.com/paper/2411.19140/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 36 references that are in the map.
- First posted on arXiv 28 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Jailbroken: How Does LLM Safety Training Fail?](https://mlsanity.com/paper/2307.02483/) (2023)
- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://mlsanity.com/paper/2310.08419/) (2023)
- [Jailbreaking Leading Safety-Aligned LLMs with Simple Adaptive Attacks](https://mlsanity.com/paper/2404.02151/) (2024)
- [The Creation and Detection of Deepfakes: A Survey](https://mlsanity.com/paper/2004.11138/) (2020)
- [Dialect prejudice predicts AI decisions about people's character, employability, and criminality](https://mlsanity.com/paper/2403.00742/) (2024)
- [Auditing Elon Musk's Impact on Hate Speech and Bots](https://mlsanity.com/paper/2304.04129/) (2023)

## Built on by (most cited first, 3 of 3)

- [Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental](https://mlsanity.com/paper/2503.16534/) (2025)
- [Neutral Prompts, Non-Neutral People: Quantifying Gender and Skin-Tone Bias in Gemini Flash 2.5 Image and GPT Image 1.5](https://mlsanity.com/paper/2602.12133/) (2026)
- [Testing for Causal Fairness](https://mlsanity.com/paper/2502.12874/) (2025)

## Abstract (arXiv)

This study investigates ChatGPT-4o's multimodal content generation, highlighting significant disparities in its treatment of sexual content and nudity versus violent and drug-related themes. Detailed analysis reveals that ChatGPT-4o consistently censors sexual content and nudity, while showing leniency towards violence and drug use. Moreover, a pronounced gender bias emerges, with female-specific content facing stricter regulation compared to male-specific content. This disparity likely stems from media scrutiny and public backlash over past AI controversies, prompting tech companies to impose stringent guidelines on sensitive issues to protect their reputations. Our findings emphasize the urgent need for AI systems to uphold genuine ethical standards and accountability, transcending mere political correctness. This research contributes to the understanding of biases in AI-driven language and multimodal models, calling for more balanced and ethical content moderation practices.