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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

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  • Kekez, Ivan
  • Lauwaert, Lode
  • Begičević Ređep, Nina

Abstract

This paper presents a systematic review of 64 papers using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) of research on bias and discrimination in the context of using Artificial Intelligence (AI). Specifically, while limiting the scope to research in HRM, it aims to answer three questions that are relevant to the research community. The first question is whether research papers define the terms 'bias' and 'discrimination', and if so how. Second, given that there are different forms of bias and discrimination, the question is exactly which ones are being investigated. Are there any forms of bias and discrimination that are underrepresented? The third question is whether a negativity bias exists in research on bias and discrimination in the context of AI. The answers to the first two questions point to some research problems. The review shows that in a substantial number of papers, the terms 'bias' and 'discrimination' are not or hardly defined. Furthermore, there is a disproportionate focus among researchers on bias and discrimination related to skin tone (racism) and gender (sexism). In the discussion, we provide reasons why this is undesirable for both scientific and extratheoretical reasons. The answer to the last question is negative. There is a relatively good balance between research that zooms in on the positive effects of AI on bias and discrimination, and research that deals with AI leading to (more) bias and discrimination.

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  • Kekez, Ivan & Lauwaert, Lode & Begičević Ređep, Nina, 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, Elsevier, vol. 81(C).
  • Handle: RePEc:eee:teinso:v:81:y:2025:i:c:s0160791x25000089
    DOI: 10.1016/j.techsoc.2025.102818
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    1. Thomas Bolander, 2019. "What do we loose when machines take the decisions?," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 23(4), pages 849-867, December.
    2. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    3. Roland G. Fryer & Steven D. Levitt, 2004. "The Causes and Consequences of Distinctively Black Names," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(3), pages 767-805.
    4. Roger E. Backhouse & Béatrice Cherrier, 2019. "Paul Samuelson, gender bias and discrimination," The European Journal of the History of Economic Thought, Taylor & Francis Journals, vol. 26(5), pages 1053-1080, September.
    5. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
    6. Zhisheng Chen, 2023. "Ethics and discrimination in artificial intelligence-enabled recruitment practices," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-12, December.
    7. Janet Gao & Wenting Ma & Qiping Xu, 2023. "Access to Financing and Racial Pay Gap Inside Firms," Working Papers 23-36, Center for Economic Studies, U.S. Census Bureau.
    8. Eleonora Veglianti & Matteo Trombin & Roberta Pinna & Marco Marco, 2023. "Customized Artificial Intelligence for Talent Recruiting: A Bias-Free Tool?," Lecture Notes in Information Systems and Organization, in: Cinzia Dal Zotto & Afshin Omidi & Georges Aoun (ed.), Smart Technologies for Organizations, pages 245-261, Springer.
    9. Scott David WILLIAMS, 2020. "A Textual Analysis Of Racial Considerations In Human Resource Analytics Vendors’ Marketing," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 12(4), pages 49-63, December.
    10. Nir Kshetri, 2021. "Evolving uses of artificial intelligence in human resource management in emerging economies in the global South: some preliminary evidence," Management Research Review, Emerald Group Publishing Limited, vol. 44(7), pages 970-990, January.
    11. Prithwiraj Choudhury & Evan Starr & Rajshree Agarwal, 2020. "Machine learning and human capital complementarities: Experimental evidence on bias mitigation," Strategic Management Journal, Wiley Blackwell, vol. 41(8), pages 1381-1411, August.
    12. Kanchana Ruwanpura, 2008. "Multiple identities, multiple-discrimination: A critical review," Feminist Economics, Taylor & Francis Journals, vol. 14(3), pages 77-105.
    13. Lennart Hofeditz & Sünje Clausen & Alexander Rieß & Milad Mirbabaie & Stefan Stieglitz, 2022. "Applying XAI to an AI-based system for candidate management to mitigate bias and discrimination in hiring," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2207-2233, December.
    14. González-Albo, Borja & Bordons, María, 2011. "Articles vs. proceedings papers: Do they differ in research relevance and impact? A case study in the Library and Information Science field," Journal of Informetrics, Elsevier, vol. 5(3), pages 369-381.
    15. Nima Kordzadeh & Maryam Ghasemaghaei, 2022. "Algorithmic bias: review, synthesis, and future research directions," European Journal of Information Systems, Taylor & Francis Journals, vol. 31(3), pages 388-409, May.
    16. Mehdi Barati & Bahareh Ansari, 2022. "Effects of algorithmic control on power asymmetry and inequality within organizations," Journal of Management Control: Zeitschrift für Planung und Unternehmenssteuerung, Springer, vol. 33(4), pages 525-544, December.
    17. Kay Jowers & Lala Ma & Christopher D. Timmins, 2023. "Racial Gaps in Federal Flood Buyout Compensations," AEA Papers and Proceedings, American Economic Association, vol. 113, pages 451-455, May.
    18. Nir Kshetri, 2021. "Evolving uses of artificial intelligence in human resource management in emerging economies in the global South: some preliminary evidence," Management Research Review, Emerald Group Publishing Limited, vol. 44(7), pages 970-990, January.
    19. Da Hoang & Duong Trung Le & Ha Nguyen & Mr. Nikola Spatafora, 2024. "Shedding Light on the Local Impact of Temperature," IMF Working Papers 2024/178, International Monetary Fund.
    20. My Nguyen & Kien Le, 2023. "Racial/ethnic match and student–teacher relationships," Bulletin of Economic Research, Wiley Blackwell, vol. 75(2), pages 393-412, April.
    21. Wonhyuk Cho & Seeyoung Choi & Hemin Choi, 2023. "Human Resources Analytics for Public Personnel Management: Concepts, Cases, and Caveats," Administrative Sciences, MDPI, vol. 13(2), pages 1-22, January.
    22. Trocin, Cristina & Hovland, Ingrid Våge & Mikalef, Patrick & Dremel, Christian, 2021. "How Artificial Intelligence affords digital innovation: A cross-case analysis of Scandinavian companies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    23. Shashidharan Shanmugam & Lalit Garg, 2015. "Model Employee Appraisal System with Artificial Intelligence Capabilities," Journal of Cases on Information Technology (JCIT), IGI Global, vol. 17(3), pages 30-40, July.
    24. Lixuan Zhang & Clinton Amos, 2024. "Dignity and use of algorithm in performance evaluation," Behaviour and Information Technology, Taylor & Francis Journals, vol. 43(2), pages 401-418, January.
    25. Matthew J Page & Joanne E McKenzie & Patrick M Bossuyt & Isabelle Boutron & Tammy C Hoffmann & Cynthia D Mulrow & Larissa Shamseer & Jennifer M Tetzlaff & Elie A Akl & Sue E Brennan & Roger Chou & Jul, 2021. "The PRISMA 2020 statement: An updated guideline for reporting systematic reviews," PLOS Medicine, Public Library of Science, vol. 18(3), pages 1-15, March.
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