This study analyzes Gemini 2.0 Flash Experimental for gender bias and content moderation practices, providing key insights for security professionals and AI ethics teams.

• Gemini 2.0 demonstrates reduced gender bias with significantly higher acceptance rates for female-specific prompts
• The model shows distinct moderation patterns when handling potentially harmful or violent content compared to ChatGPT-4o
• Findings reveal important differences in how leading LLMs implement ethical guardrails and content filtering

For security professionals, this research highlights evolving standards in AI safety mechanisms and content moderation approaches that directly impact deployment risks and user experiences.

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