From Gender Discrimination to Algorithmic Bias: Evolution of Gender Bias Research in Human Resource Management
DOI:
https://doi.org/10.32493/JJSDM.v10i1.63660Keywords:
Gender Discrimination, Gender Bias, Human Resource Management.Abstract
This study aims to map the evolution of gender discrimination research in Human Resource Management (HRM) through a combination of bibliometric analysis and Systematic Literature Review (SLR). Data were collected from the Scopus database using the keywords "gender bias" OR "gender discrimination" OR "gender stereotype". Following the PRISMA 2020 guidelines, 4.492 journal articles published between 1973 - 2025 were selected for bibliometric analysis, while 56 highly relevant studies were further examined through an in-depth SLR. Bibliometric analysis was conducted using VOSviewer to identify publication trends, keyword networks, and thematic structures within the field. The findings reveal a substantial increase in publications on gender bias in HRM, particularly after 2020. The literature is primarily concentrated on themes such as gender equality, workplace discrimination, diversity and inclusion, leadership, glass ceiling, and work–life balance. Furthermore, emerging topics including intersectionality, gender diversity, corporate governance, well being, inclusion, and artificial intelligence have gained increasing scholarly attention. The analysis also indicates a shift in research focus from traditional forms of gender discrimination toward technology-driven concerns, particularly algorithmic bias in HRM practices. This study contributes by providing a comprehensive overview of the intellectual structure, research trends, and future directions of gender bias research in HRM.
References
1. Ahn, J., Kim, J., & Sung, Y. (2022). The effect of gender stereotypes on artificial intelligence recommendations. Journal of Business Research, 141, 50–59. https://doi.org/10.1016/j.jbusres.2021.12.007
2. Akter, S., Dwivedi, Y. K., Biswas, K., Michael, K., Bandara, R. J., & Sajib, S. (2021). Addressing Algorithmic Bias in AI-Driven Customer Management. Journal of Global Information Management, 29(6). https://doi.org/10.4018/JGIM.20211101.oa3
3. Banks, G. C., Tonidandel, S., Dou, W., Gerson, M. J., Xu, D., & Yavorsky, J. E. (2025). How to Reduce Bias in the Life Cycle of a Data Science Project. Journal of Business and Psychology, 40(6), 1253–1273. https://doi.org/10.1007/s10869-025-10022-x
4. Bansal, C., Pandey, K. K., Goel, R., Sharma, A., & Jangirala, S. (2023). Artificial intelligence (AI) bias impacts: classification framework for effective mitigation. Issues in Information Systems, 24(4), 367–389. https://doi.org/10.48009/4_iis_2023_128
5. Beauvoir, S. de. (1949). The second sex (C. Borde & S. Malovany-Chevallier, Trans.). Vintage Books.
6. Bhatt, P., Singh, T., Pavlopoulos, V., & Pham, H. (2025). Decoding Sentiments: Unveiling AI-Generated Content Through Sentiment Analysis and Human Evaluation. Journal of Global Information Management, 33(1). https://doi.org/10.4018/JGIM.394505
7. Bilon, X. J. (2025). Sociodemographic biases in employment decision-making by large language models: experimental evidence from ChatGPT simulations. Journal of Decision Systems, 34(1). https://doi.org/10.1080/12460125.2025.2599912
8. Borau, S., Otterbring, T., Laporte, S., & Fosso Wamba, S. (2021). The most human bot: Female gendering increases humanness perceptions of bots and acceptance of AI. Psychology and Marketing, 38(7), 1052–1068. https://doi.org/10.1002/mar.21480
9. Branicki, L. J. (2020). COVID-19 , ethics of care and feminist crisis management. Gender Work Organ, 27, 872–883. https://doi.org/10.1111/gwao.12491
10. Brauner, P., Glawe, F., Liehner, G. L., Vervier, L., & Ziefle, M. (2025). Mapping public perception of artificial intelligence: Expectations, risk–benefit tradeoffs, and value as determinants for societal acceptance. Technological Forecasting and Social Change, 220. https://doi.org/10.1016/j.techfore.2025.124304
11. Calabrese, A., Carrino, S., Costa, R., Roberti, E., & Tiburzi, L. (2026). Exploring the Impact of Generative AI in Detecting Biases and Discrimination in Job Advertisements. Corporate Social Responsibility and Environmental Management. https://doi.org/10.1002/csr.70618
12. Cao, X., Li, H., Xu, Q., & Zhu, R. (2025). Detecting Gender Stereotype Biases Against Women Entrepreneurs in Large Language Models. Journal of Business Ethics. https://doi.org/10.1007/s10551-025-06216-1
13. Cardel, M. I., Dhurandhar, E., Yarar-Fisher, C., Foster, M., Hidalgo, B., McClure, L. A., Pagoto, S., Brown, N., Pekmezi, D., Sharafeldin, N., Willig, A. L., & Angelini, C. (2020). Turning Chutes into Ladders for Women Faculty: A Review and Roadmap for Equity in Academia. Journal of Women’s Health, 29(5), 721–733. https://doi.org/10.1089/jwh.2019.8027
14. Castro-Martínez, A., Torres-Martín, J.-L., & Pérez-Ordóñez, C. (2025). Class, gender and race stereotypes reproduced by generative AI: recommendations for users. Encontros Bibli, 30. https://doi.org/10.5007/1518-2924.2025.e103518
15. Cecere, G., Jean, C., Le Guel, F., & Manant, M. (2024). Artificial intelligence and algorithmic bias? Field tests on social network with teens. Technological Forecasting and Social Change, 201. https://doi.org/10.1016/j.techfore.2023.123204
16. Chordiya, R., & Hubbell, L. (2023). Fostering Internal Pay Equity Through Gender Neutral Job Evaluations: A Case Study of the Federal Job Evaluation System. Public Personnel Management, 52(1), 25–47. https://doi.org/10.1177/00910260221124866
17. de Lima, R. M., Pisker, B., & Corrêa, V. S. (2023). Gender Bias in Artificial Intelligence A Systematic Review of the Literature. Journal of Telecommunications and the Digital Economy, 11(2), 8–31. https://doi.org/10.18080/jtde.v11n2.690
18. Fedele, A., Punzi, C., & Tramacere, S. (2024). The ALTAI checklist as a tool to assess ethical and legal implications for a trustworthy AI development in education. Computer Law and Security Review, 53. https://doi.org/10.1016/j.clsr.2024.105986
19. Feldkamp, T., Langer, M., Wies, L., & König, C. J. (2024). Justice, trust, and moral judgements when personnel selection is supported by algorithms. European Journal of Work and Organizational Psychology, 33(2), 130–145. https://doi.org/10.1080/1359432X.2023.2169140
20. Figueiredo, H., Rocha, V., Biscaia, R., & Teixeira, P. (2015). Gender pay gaps and the restructuring of graduate labour markets in Southern Europe. Cambridge Journal of Economics, 39(2), 565–598. https://doi.org/10.1093/cje/bev008
21. Furtado, J. V, & Moreira, C. (2021). Gender Affirmative Action and Management : A Systematic Literature Review on How Diversity and Inclusion Management Affect Gender Equity in Organizations. Behavioral Sciences, 11, 21. https://doi.org/10.3390/bs11020021
22. He, J., Lin, N., Bai, Q., Liang, H., Zhou, D., & Yang, A. (2024). Towards fair decision: A novel representation method for debiasing pre-trained models. Decision Support Systems, 181. https://doi.org/10.1016/j.dss.2024.114208
23. Hing, L. S. S., Sakr, N., Sorenson, J. B., Stamarski, C. S., Caniera, K., & Colaco, C. (2023). Human Resource Management Review Gender inequities in the workplace: A holistic review of organizational processes and practices. Human Resource Management Review, 33(3), 100968. https://doi.org/10.1016/j.hrmr.2023.100968
24. Hurlin, C., Pérignon, C., & Saurin, S. (2024). The Fairness of Credit Scoring Models. Management Science, 72(1), 406–425. https://doi.org/10.1287/mnsc.2022.03888
25. Igarashi, A., Kano, Y., & Miwa, H. (2025). ChatGPT versus humans in judging discriminatory scenarios: experimental evidence from a Japanese context. Humanities and Social Sciences Communications, 12(1). https://doi.org/10.1057/s41599-025-06054-6
26. Inga, K., Storm, L., Katharina, L., Anna, E., Clar-novak, M., & Louise, S. (2023). Human Resource Management Review Unconscious bias in the HRM literature : Towards a critical-reflexive approach. Human Resource Management Review, 33(3), 100969. https://doi.org/10.1016/j.hrmr.2023.100969
27. Jagannathan, R., Camasso, M. J., & LaFleur, J. (2024). Gender pay gaps in the young adult labor force: prejudice-based discrimination or misreading of the observed-to-offered wage relationship? Oxford Economic Papers, 76(4), 1168–1188. https://doi.org/10.1093/oep/gpae009
28. Jiang, L., Cao, J., Zhu, G., & Wang, Y. (2026). Exploring attribution bias in LLMs: Social influences and prompt-based mitigation. Technology in Society, 87. https://doi.org/10.1016/j.techsoc.2026.103361
29. John-Mathews, J.-M. (2022). Some critical and ethical perspectives on the empirical turn of AI interpretability. Technological Forecasting and Social Change, 174. https://doi.org/10.1016/j.techfore.2021.121209
30. Kekez, I., Lauwaert, L., & Begičević Ređep, N. (2025). Is artificial intelligence (AI) research biased and conceptually vague? A systematic review of research on bias and discrimination in the context of using AI in human resource management. Technology in Society, 81. https://doi.org/10.1016/j.techsoc.2025.102818
31. Kelan, E. K. (2025). Man-versus-machine: gender and technology in discourses on the future of work. Gender in Management. https://doi.org/10.1108/GM-09-2024-0513
32. Kelley, S., Ovchinnikov, A., Hardoon, D. R., & Heinrich, A. (2022). Antidiscrimination Laws, Artificial Intelligence, and Gender Bias: A Case Study in Nonmortgage Fintech Lending. Manufacturing and Service Operations Management, 24(6), 3039–3059. https://doi.org/10.1287/msom.2022.1108
33. Lambrecht, A., & Tucker, C. (2019). Algorithmic bias? An empirical study of apparent gender-based discrimination in the display of stem career ads. Management Science, 65(7), 2966–2981. https://doi.org/10.1287/mnsc.2018.3093
34. Lane, J. N., Lakhani, K. R., & Fernandez, R. M. (2024). Setting Gendered Expectations? Recruiter Outreach Bias in Online Tech Training Programs. Organization Science, 35(3), 911–927. https://doi.org/10.1287/orsc.2022.16499
35. Lindsay, R. (2021). Gender-Based Pay Discrimination in Otolaryngology. Laryngoscope, 131(5), 989–995. https://doi.org/10.1002/lary.29103
36. Magliano, D. J., Mace, V. G., Ellis, T. M., & Calkin, A. C. (2020). Addressing Gender Equity in Senior Leadership Roles in Translational Science. American Chemical Society, 3, 773−779. https://doi.org/10.1021/acsptsci.0c00056
37. Mahajan, S., Agarwal, R., & Gupta, M. (2025). Algorithmic Bias Under the EU AI Act: Compliance Risk, Capital Strain, and Pricing Distortions in Life and Health Insurance Underwriting. Risks, 13(9). https://doi.org/10.3390/risks13090160
38. Majrashi, K. (2025). Employees’ perceptions of the fairness of AI-based performance prediction features. Cogent Business and Management, 12(1). https://doi.org/10.1080/23311975.2025.2456111
39. Malik, R. D., & Salles, A. (2022). Debunking Four Common Gender Equity Myths. In European Urology (Vol. 81, Number 6, pp. 552–554). Elsevier B.V. https://doi.org/10.1016/j.eururo.2022.02.019
40. Milkau, U. (2023). Is algorithmic credit scoring a “high risk”? Journal of Digital Banking, 7(3), 249–265. https://doi.org/10.69554/BYMQ1790
41. Muñoz-García, V., Consuegra-Ayala, J. P., & Moreda, P. (2025). Leading and non-leading prompts: Quantifying gender bias in Large Language Models through BiasBloom corpus. Knowledge-Based Systems, 325. https://doi.org/10.1016/j.knosys.2025.113915
42. Nadeem, A., Marjanovic, O., & Abedin, B. (2022). Gender bias in AI-based decision-making systems: a systematic literature review. Australasian Journal of Information Systems, 26. https://doi.org/10.3127/AJIS.V26I0.3835
43. Naguib, R., & Madeeha, M. (2023). Women’s Studies International Forum “Making visible the invisible”: Exploring the role of gender biases on the glass ceiling in Qatar’s public sector. Women’s Studies International Forum, 98(March), 102723. https://doi.org/10.1016/j.wsif.2023.102723
44. Newstead, T., Eager, B., & Wilson, S. (2023). How AI can perpetuate – Or help mitigate – Gender bias in leadership. Organizational Dynamics, 52(4). https://doi.org/10.1016/j.orgdyn.2023.100998
45. Pagano, T. P., Loureiro, R. B., Lisboa, F. V. N., Cruz, G. O. R., Peixoto, R. M., Guimarães, G. A. D. S., Oliveira, E. L. S., Winkler, I., & Nascimento, E. G. S. (2023). Context-Based Patterns in Machine Learning Bias and Fairness Metrics: A Sensitive Attributes-Based Approach. Big Data and Cognitive Computing, 7(1). https://doi.org/10.3390/bdcc7010027
46. Pavone, G., & Desveaud, K. (2025). Gendered AI in fully autonomous vehicles: the role of social presence and competence in building trust. Journal of Consumer Marketing, 42(2), 240–254. https://doi.org/10.1108/JCM-05-2024-6865
47. Powell, D. Anthony Butterfield, Powell, G. N., & Butterfield, D. A. (1979). The “good manager”: Masculine or androgynous? Academy of Management Journal, 22, 395–403. https://doi.org/10.1177/1059601192171004
48. Ren, R., Xu, Y., Yao, X., & Cole, S. T. (2025). Whose journey matters? Investigating identity biases in large language models (LLMs) for travel planning assistance. Current Issues in Tourism. https://doi.org/10.1080/13683500.2025.2609218
49. Rie, S. K. (2021). Perspective Gender bias in academia: A lifetime problem that needs solutions. Neuron, 109, 2047–2074. https://doi.org/10.1016/j.neuron.2021.06.002
50. Rosen, B., & Jerdee, T. H. (1974). Effects of applicant’s sex and difficulty of job on evaluations of candidates for managerial positions. Journal of Applied Psychology, 59(4), 511–512. https://doi.org/10.1037/h0037323
51. Ryan, M. K. (2023). Addressing workplace gender inequality: Using the evidence to avoid common pitfalls. Br J Soc Psychol., 62, 1–11. https://doi.org/10.1111/bjso.12606
52. Schein, V. E. (1973). The relationship between sex role stereotypes and requisite management characteristics. Journal of Applied Psychology, 57(2), 95–100. https://doi.org/10.1037/h0037128
53. Slimi, Z., & Carballido, B. V. (2023). Navigating the Ethical Challenges of Artificial Intelligence in Higher Education: An Analysis of Seven Global AI Ethics Policies. TEM Journal, 12(2), 590–602. https://doi.org/10.18421/TEM122-02
54. Song, Y. J., Park, J., & Lee, S. K. (2026). Disappointed with Siri: Expectation–experience gaps in human–AI interaction. Technological Forecasting and Social Change, 225. https://doi.org/10.1016/j.techfore.2026.124540
55. Stamarski, C. S., Hing, L. S. S., & Elacqua, T. C. (2015). Gender inequalities in the workplace : the effects of organizational structures , processes , practices , and decision makers ’ sexism. Frontiers in Psychology, 6(September), 1–20. https://doi.org/10.3389/fpsyg.2015.01400
56. Trautwein, Y., Zechiel, F., Coussement, K., Meire, M., & Büttgen, M. (2025). Opening the “black box” of HRM algorithmic biases – How hiring practices induce discrimination on freelancing platforms. Journal of Business Research, 192. https://doi.org/10.1016/j.jbusres.2025.115298
57. Tricco, A. C., Parker, A., Khan, P. A., Nincic, V., Robson, R., Macdonald, H., Warren, R., Cleary, O., Zibrowski, E., Baxter, N., Burns, K. E. A., Coyle, D., Ndjaboue, R., Clark, J. P., Langlois, E. V, Ahmed, S. B., Witteman, H. O., & Graham, I. D. (2024). Interventions on gender equity in the workplace : a scoping review. BMC Medicine, 1–12. https://doi.org/10.1186/s12916-024-03346-7
58. Tsui, A. S., & O’Reilly, C. A. (1989). Beyond Simple Demographic Effects: The Importance of Relational Demography in Superior-Subordinate Dyads. Academy of Management Journal, 32, 402–423. https://doi.org/10.2307/256368
59. Tsung-Yu, H., Yu-Chia, T., & Chien Wen (Tina), Y. (2024). Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users' bias perceptions and trust. International Journal of Information Management, 76. https://doi.org/10.1016/j.ijinfomgt.2024.102775
60. Varsha. (2023). How can we manage biases in artificial intelligence systems – A systematic literature review. International Journal of Information Management Data Insights, 3(1). https://doi.org/10.1016/j.jjimei.2023.100165
61. Weber-Lewerenz, B., & Vasiliu-Feltes, I. (2022). Empowering Digital Innovation by Diverse Leadership in ICT – A Roadmap to a Better Value System in Computer Algorithms. Humanistic Management Journal, 7(1), 117–134. https://doi.org/10.1007/s41463-022-00123-7
62. Wiedman, C. (2020). Rewarding Collaborative Research: Role Congruity Bias and the Gender Pay Gap in Academe. Journal of Business Ethics, 167(4), 793–807. https://doi.org/10.1007/s10551-019-04165-0
63. Zaidi, M., Amiruddin, B., Samsudin, A., Suhandi, A., Cos, B., & Prahani, B. K. (2025). Social Sciences & Humanities
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