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

ABSTRACT: This study evaluates the biases in Gemini 2.0 Flash Experimental, a state-of-the-art large language model (LLM) developed by Google, focusing on content moderation and gender disparities. By comparing its performance to ChatGPT-4o, examined in a previous work of the author, the analysis highlights some differences in ethical moderation practices. Gemini 2.0 demonstrates reduced gender bias, notably with female-specific prompts achieving a substantial rise in acceptance rates compared to results obtained by ChatGPT-4o. It adopts a more permissive stance toward sexual content and maintains relatively high acceptance rates for violent prompts (including gender-specific cases). Despite these changes, whether they constitute an improvement is debatable. While gender bias has been reduced, this

SUBMITTER: Balestri R 

PROVIDER: S-EPMC11958708 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature