Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental

Area of Science:

• Artificial Intelligence
• Computer Science
• Ethics in AI

Background:

• Large language models (LLMs) require robust content moderation.
• Previous work analyzed ChatGPT-4o's ethical moderation practices.
• Assessing biases in newer LLMs like Gemini 2.0 is crucial.

Purpose of the Study:

• Evaluate content moderation biases in Gemini 2.0 Flash Experimental.
• Compare Gemini 2.0's gender bias and ethical stances with ChatGPT-4o.
• Analyze the implications of Gemini 2.0's moderation policies.

Main Methods:

• Comparative analysis of Gemini 2.0 Flash Experimental and ChatGPT-4o.
• Testing with gender-specific prompts for content moderation.
• Evaluation of acceptance rates for sexual and violent content.

Main Results:

• Gemini 2.0 exhibits reduced gender bias compared to ChatGPT-4o, with higher acceptance for female-specific prompts.
• Gemini 2.0 shows increased permissiveness toward sexual content.
• Gemini 2.0 maintains high acceptance rates for violent content, affecting both genders.
• Male-specific prompts still generally have higher acceptance rates than female-specific ones.

Conclusions:

• Gemini 2.0 reduces gender bias but increases permissiveness for violent content, posing ethical dilemmas.
• The trade-off between bias reduction and content permissiveness requires careful consideration.
• Further AI model refinement is necessary for transparent, fair, and inclusive moderation.