An Empirical Investigation of Gender Stereotype Representation in Large Language Models: The Italian Case

TL;DR

This study empirically examines gender stereotypes in Italian language responses of LLMs, revealing biases that could influence societal perceptions and emphasizing the need for mitigation strategies.

Contribution

It provides a structured experimental analysis of gender bias in Italian LLMs, focusing on professional stereotypes and language-specific challenges.

Findings

Gemini associates 100% of 'she' pronouns with 'assistant'

ChatGPT associates 97% of 'she' pronouns with 'assistant'

Biases in LLM responses can reinforce stereotypes and social inequalities.

Abstract

The increasing use of Large Language Models (LLMs) in a large variety of domains has sparked worries about how easily they can perpetuate stereotypes and contribute to the generation of biased content. With a focus on gender and professional bias, this work examines in which manner LLMs shape responses to ungendered prompts, contributing to biased outputs. This analysis uses a structured experimental method, giving different prompts involving three different professional job combinations, which are also characterized by a hierarchical relationship. This study uses Italian, a language with extensive grammatical gender differences, to highlight potential limitations in current LLMs' ability to generate objective text in non-English languages. Two popular LLM-based chatbots are examined, namely OpenAI ChatGPT (gpt-4o-mini) and Google Gemini (gemini-1.5-flash). Through APIs, we collected a range of 3600 responses. The results highlight how content generated by LLMs can perpetuate stereotypes. For example, Gemini associated 100% (ChatGPT 97%) of 'she' pronouns to the 'assistant' rather than the 'manager'. The presence of bias in AI-generated text can have significant implications in many fields, such as in the workplaces or in job selections, raising ethical concerns about its use. Understanding these risks is pivotal to developing mitigation strategies and assuring that AI-based systems do not increase social inequalities, but rather contribute to more equitable outcomes. Future research directions include expanding the study to additional chatbots or languages, refining prompt engineering methods or further exploiting a larger experimental base.