Investigating Gender Biases in High-level Recruitment: An NLP-driven Approach to Analyzing Job Postings

This study investigates the potential of Natural Language Processing (NLP) to identify gender biases in recruitment for senior management positions, addressing a critical factor contributing to the underrepresentation of women in corporate leadership.

By analyzing job advertisements, this research aims to uncover how gendered language perpetuates biases in recruitment processes and explore innovative solutions to foster diversity in high decision-making roles.

The study conducts an experiment analyzing (N. 2,198) job offers published in February and September 2021 in the Financial Times. Using NLP techniques, it identifies frequent terms and their occurrence rates in job postings to detect gender biases. The analysis applies agentic-communal role differentiation rooted in Social Identity, Homosocial, and Think Manager-Think Male (TM-TM) theories to understand how language reinforces traditional gender roles.

The results reveal a strong co-occurrence of terms associated with male roles in the studied sectors, demonstrating the persistence of gendered language in job advertisements. This study extends prior work by showcasing the unique application of NLP in management studies to analyze large volumes of documents, offering a novel method for identifying biases. It highlights that male-oriented language remains a barrier to women’s access to high-level positions.

Practical implications include recommendations for organizations and recruiters to adopt neutral language in job postings and promote cultural changes toward inclusivity.

This study contributes to the discourse on gender equity by providing actionable insights into how NLP can address biases in recruitment processes. It emphasizes the need for organizations to reevaluate selection processes and implement strategies that foster more inclusive workplaces, ultimately bridging the gender gap in corporate leadership.