Gender Bias in Generative AI-assisted Recruitment Processes

TL;DR

This study investigates gender bias in generative AI used for recruitment, revealing that while job suggestions are unbiased, linguistic patterns reflect stereotypical gender traits, raising ethical concerns.

Contribution

It provides an empirical analysis of gendered language bias in GPT-5's job suggestion outputs for simulated profiles, highlighting ethical implications.

Findings

No significant gender bias in job titles or industries.

Gendered adjectives reflect stereotypes: women linked to emotional traits, men to strategic traits.

Raises ethical concerns about bias in AI-driven recruitment.

Abstract

In recent years, generative artificial intelligence (GenAI) systems have assumed increasingly crucial roles in selection processes, personnel recruitment and analysis of candidates' profiles. However, the employment of large language models (LLMs) risks reproducing, and in some cases amplifying, gender stereotypes and bias already present in the labour market. The objective of this paper is to evaluate and measure this phenomenon, analysing how a state-of-the-art generative model (GPT-5) suggests occupations based on gender and work experience background, focusing on under-35-year-old Italian graduates. The model has been prompted to suggest jobs to 24 simulated candidate profiles, which are balanced in terms of gender, age, experience and professional field. Although no significant differences emerged in job titles and industry, gendered linguistic patterns emerged in the adjectives attributed to female and male candidates, indicating a tendency of the model to associate women with emotional and empathetic traits, while men with strategic and analytical ones. The research raises an ethical question regarding the use of these models in sensitive processes, highlighting the need for transparency and fairness in future digital labour markets.