Unveiling Gender Biases in Recruitment: A Natural Language Processing Approach

Mirian Izquierdo Barriuso *

University of Witten-Herdecke, Germany.

*Author to whom correspondence should be addressed.


This paper investigates the potential of AI to identify gender biases in recruitment for senior management positions in businesses, dealing with many documents. It aims to unravel the impact of gender biases in job advertisements as a possible reason behind the underrepresentation of women in the corporate world. An innovative experiment that extracts and analyses 2.198 job offers published in February and September 2021 in the Financial Times newspaper is presented. Natural language techniques are used. These methods identify the most frequent terms and their appearance rate in the advertisements showing the gender biases they generate. By enabling the analysis of many documents, the method allows the accurate identification of gender biases. This use is unique in management studies. The results show a strong co-occurrence of terms associated with male roles in the studied sectors. The concept of agentic-communal role differentiation, rooted in the Identity, homosocial, and TM-TM theories, supports the findings. This knowledge will contribute to using NLP to discover gender biases in recruitment for high decision-making positions proposing action to improve the present situation. Should more women ascend to decision-making positions, selection processes should be improved to reflect a more neutral language. At the same time, cultural changes should be promoted in the corporate world toward more inclusive workplaces.

Keywords: Natural language processing techniques, social identity, homosocial and TM-TM theories, job postings, recruitment

How to Cite

Barriuso, M. I. (2024). Unveiling Gender Biases in Recruitment: A Natural Language Processing Approach. Journal of Global Economics, Management and Business Research, 16(1), 19–38. https://doi.org/10.56557/jgembr/2024/v16i18634


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