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.


Abstract

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

Downloads

Download data is not yet available.

References

Foley M, Williamson S. Does anonymising job applications reduce gender bias? Understanding managers’ perspectives. Gender in Management. 2018;33(8):623-635.

Stephenson AL, Dzubinski LM, Diehl AB. A cross-industry comparison of how women leaders experience gender bias. Personnel Review; 2022.

Bhardwaj S. Decision-making in the recruitment of women on corporate boards: Does gender matter? Equality, Diversity and Inclusion. 2022;41(6):813-830 Available:https://doi.org/10.1108/EDI-08-2021-0188

Campion MC, Campion MA, Campion ED, Reider MH. Initial investigation into computer scoring of candidate essays for personnel selection. Journal of Applied Psychology. 2016;101(7):958-975.

Available:https://doi.org/10.1037/apl0000108

Langer M, Landers RN. The future of artificial intelligence at work: A review on effects of decision automation and augmentation on workers targeted by algorithms and third-party observers. Computers in Human Behavior. 2021 ;123(106878) Available:https://doi.org/10.1016/j.chb.2021.106878

Gonzalez MF, Liu W, Shirase L, Tomczak DL, Lobbe CE, Justenhoven R, Martin NR. Allying with AI? Reactions toward human-based, AI/ML-based, and augmented hiring processes. Computers in Human Behavior. 2022;130(107179) Available:https://doi.org/10.1016/j.chb.2022.107179

Manning CD, Schütze H. Lexical acquisition. Foundations of statistical natural language processing. 1999; 999:296-305.

Raghavan M, Barocas S, Kleinberg J, Levy K. Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. 2020;469-481.

Oswald FL, Behrend TS, Putka DJ, Sinar E. Big data in industrial-organizational psychology and human resource management: Forward progress for organizational research and practice. Annual Review of Organizational Psychology and Organizational Behavior. 2020;7(1):505–533 Available:https://doi.org/10.1146/annurev-orgpsych-032117-104553

Nissenbaum H, Freidman B. Bias in computer systems. ACM Trans. Inf. Syst. 1996;14(3):330–347 Available:https://doi.org/10.1145/230538.230561

Campolo A, Sanfilippo M, Whittaker M, Crawford K. AI Now 2017 Report. AI Now Institute; 2017.

Bohnet I. What works: Gender equality by design. Cambridge, MA: Belknap Press; 2016.

Bolukbasi T, Chang KW, Zou JY, Saligrama V, Kalai AT. Man is to computer programmer as woman is to homemaker? Debiasing Word Embeddings. Advances in neural information processing systems. 2016;29.

Zemel R, Wu Y, Swersky K, Pitassi T.amp; Dwork C. Learning fair representations. Proceedings of the 30th international conference on machine learning in proceedings of machine learning research. 2013;28(3):325-333 Available:https://proceedings.mlr.press/v28/zemel13.html

Hardt M, Price E, Srebro N. Equality of opportunity in supervised learning. Advances in neural information processing systems. 2016;29.

Garg N, Schiebinger L, Jurafsky D, Zou J. Word embeddings quantify 100 years of gender and ethnic stereotypes. Proceedings of the National Academy of Sciences. 2018;115(16):E3635-E3644.

Wang Y, Blei D, Cunninngham JP. Posterior collapse and latent variable non-identifiability. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang & J. Wortman Vaughan, Advances in Neural Information Processing Systems 34. NeurIPS; 2021.

Carli LL. Gender, language, and influence. Journal of Personality and Social Psychology. 1990;59(5):941.

Lakoff R. Linguistic theory and the real world 1. Language Learning. 1975;25 (2):309-338.

Brownlow S, Rosamond JA, Parker JA. Gender-linked linguistic behavior in television interviews. Sex Roles: A Journal of Research. 2003;49(3-4):121–132 Available:https://doi.org/10.1023/A:1024404812972

Haas A. Male and female spoken language differences: Stereotypes and Evidence. Psychological Bulletin. 2007;86:616-626 Available:https://doi.org/10.1037/0033-2909.86.3.616

Leaper C, Ayres MM. A meta-analytic review of gender variations in adults' language use: Talkativeness, affiliative speech, and assertive speech. Personality and Social Psychology Review. 2007; 11(4):328–363 Available:https://doi.org/10.1177/1088868307302221

Newman ML, Groom CJ, Handelman LD, Pennebaker JW. Gender differences in language use: An analysis of 14,000 text samples. Discourse Processes. 2008;45(3):211–236 Available:https://doi.org/10.1080/01638530802073712

England P, Levine A, Mishel E. Progress toward gender equality in the United States has slowed or stalled. Proc. Natl. Acad. Sci. U.S.A. 2020;117:6990–6997 DOI: 10.1073/pnas.1918891117

Bertrand M. Gender in the twenty-first century. AEA Papers Proc. 2020;110:1–24 DOI: 10.1257/pandp.20201126

Ibarra H. Paving an alternative route: Gender differences in managerial networks. Social Psychology Quarterly. 1997;60(1):91-102.

Chattopadhyay P, Tluchowska M, George E. Identifying the ingroup: A closer look at the influence of demographic dissimilarity on employee social identity. Academy of Management Review. 2004;29(2):180-202.

Stout JG, Dasgupta N. When he doesn't mean you: Gender-exclusive language as ostracism. Personality and Social Psychology Bulletin. 2011;37(6):757-769.

Hu S, Al-Ani Jabir A, Hughes K, Denier N, Konnikov A, Ding L, Xie J, Hu Y, Tarafdar M, Jiang B, Kong L, Dai H. Balancing gender bias in job advertisements with text-level bias mitigation. Frontiers in Big Data; 2022 Available:https://www.frontiersin.org/articles/10.3389/fdata.2022.805713

Born MP, Taris TW. The impact of the wording of employment advertisements on students' inclination to apply for a job. The Journal of Social Psychology. 2010;150 (5):485–502.

Available:https://doi.org/10.1080/00224540903365422

Eagly AH. Reporting sex differences. American psychologist. 1987;42(7):756-757 Available:https://doi.org/10.1037/0003-066X.42.7.755

Cejka MA, Eagly AH. Gender-stereotypic images of occupations correspond to the sex segregation of employment. Personality and Social Psychology Bulletin. 1999;25(4):413-423.

Bem SL. Gender schema theory: A cognitive account of sex typing. Psychological Review. 1981;88(4):354-364.

Heilman ME. Description and prescription: How gender stereotypes prevent women's ascent up the organizational ladder. Journal of Social Issues. 2001;57(4):657-674.

Heilman ME, Okimoto TG. Why are women penalized for success at male tasks: The implied communality deficit. Journal of Applied Psychology. 2007;92 (1):81-92.

Rudman LA, Phelan JE. Backlash effects for disconfirming gender stereotypes in organizations. Research in Organizational Behavior. 2008;28:61-79.

Gaucher D, Friesen J, Kay AC. Evidence that gendered wording in job advertisements exists and sustains gender inequality. Journal of Personality and Social Psychology. 2011;101(1):109-128.

Fernandez-Mateo I, King Z. Anticipatory sorting and gender segregation in temporary employment. Management Science. 2011;57(6):989-1008.

Gupta D, Kumari S, Ekbal A, Bhattacharyya P. MMQA: A multi-domain multi-lingual question-answering framework for English and Hindi. In proceedings of the eleventh international conference on language resources and evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA); 2018.

Kanter RM. Men and Women of the Corporation: New Edition. New York: Basic Books; 1993.

Schein VE. A global look at psychological barriers to women’s progress in management. Journal of Social Issues. 2001;57(4):675-688.

Ollilainen M, Calasanti TM. Metaphors at work: Maintaining the salience of gender in self-managing teams. Gender, Work & Organization. 2007;14(3):267-290.

Holgersson Ch. Recruiting managing directors: Doing homosociality. Gender, Work & Organization. 2013;20 DOI: 10.1111/j.1468-0432.2012.00595.x

Schein VE. The relationship between sex role stereotypes and requisite management characteristics. Journal of Applied Psychology. 1973;57(2):95-100.

Gartzia L, Van Engen M. Are (male) leaders “feminine” enough? Gendered traits of identity as mediators of sex differences in leadership styles. Gender in Management: An International Journal; 2012.

Eagly Alice, Nater Christa, Miller David, Kaufmann, Michèle, Sczesny, Sabine. Gender stereotypes have changed: A cross-temporal meta-analysis of U.S. Public Opinion Polls From 1946 to 2018. American Psychologist. 2019;75 DOI: 10.1037/amp0000494

NLTK. Documentation; 2023, January Available:https://www.nltk.org

Wu L, Hoi SC, Yu N. Semantics-preserving bag-of-words models and applications. IEEE Transactions on Image Processing. 2010;19(7):1908-1920.

Wordcloud 1.7.0 Available:https://pypi.org/project/wordcloud/

Aizawa A. An information-theoretic perspective of tf–idf measures. Information Processing & Management. 2003;39(1):45-65.

Salton G, McGill M. Introduction to Modern Information Retrieval; 1983 Available:https://towardsdatascience.com/text-summarization-using-tf-idf-e64a0644ace3

Ashnai B, Mani S, Kothandaraman P, Shekari S. Gender bias in the recruitment of entry-level B2B salespeople. Journal of Business & Industrial Marketing. 2020;35(8):1335-1344 Available:https://doi.org/10.1108/JBIM-08-2019-0384

Quintero E. How are job applicants disadvantaged by gender based double standards in a natural setting. Cornell University; 2008.

Haslam SA, Ellemers N. Identity processes in organizations. Handbook of identity theory and research. 2011;715-744.

McDonald S. What's in the “old boys” network? Accessing social capital in gendered and racialized networks. Social Networks. 2011;33(4):317-330.

Correll SJ, Benard S, Paik I. Getting a job: Is there a motherhood penalty? American journal of sociology. 2007;112(5):1297-1338.

Rivera LA. Hiring as cultural matching: The case of elite professional service firms. American sociological review.32012;77(6):999-1022.

Gorman EH. Gender stereotypes, same-gender preferences, and organizational variation in the hiring of women: Evidence from law firms. American Sociological Review. 2005;70(4):702-728.

Kalev A, Dobbin F, Kelly E. Best practices or best guesses? Assessing the efficacy of corporate affirmative action and diversity policies. American Sociological Review. 2006;71(4):589-617.

Caliskan A, Bryson J, Narayanan A. Semantics derived automatically from language corpora contain human-like biases. Science. 2017;356(6334): 183-186.

West SM, Whittaker M, Crawford K. Discriminating systems: Gender, race, and power in AI. AI Now Institute; 2019.

Richards C, Bouman WP, Seal L, Barker MJ, Nieder TO, T’Sjoen G. Non-binary or genderqueer genders. International Review of Psychiatry. 2016;28(1):95-102.

Tang F, Kawamoto Y, Kato N, Liu J. Future intelligent and secure vehicular network toward 6G: Machine-Learning Approaches. Proceedings of the IEEE. 2020;108(2):292-307 Available:https://ieeexplore.ieee.org/document/8926369