The Application of Artificial Intelligence in Recruitment and Selection: Ethical Challenges and Effectiveness
DOI:
https://doi.org/10.55606/jumbiku.v5i1.5464Keywords:
AI Recruitment, Algorithmic Bias, Digital Hiring, Ethics In HR, TransparencyAbstract
This study explores the application of Artificial Intelligence (AI) in recruitment and selection, emphasizing both its effectiveness and ethical challenges. As organizations increasingly adopt AI driven tools to enhance hiring efficiency and decision accuracy, concerns regarding algorithmic bias, transparency, and candidate privacy continue to emerge. The research employs a qualitative literature review method to synthesize findings from recent scholarly publications, guided by frameworks such as the Technology Acceptance Model (TAM) and the Fairness, Accountability, and Transparency (FAT) principles. The study reveals that while AI significantly reduces recruitment time and improves role matching accuracy, it may also perpetuate systemic biases and undermine trust among applicants. Furthermore, the success of AI implementation depends not only on technical integration but also on ethical governance and candidate perception. Based on these insights, the research offers practical recommendations for organizations to adopt transparent, fair, and legally compliant AI recruitment practices. This paper contributes to the growing discourse on ethical digital transformation in human resource management and provides a multidimensional understanding of AI's role in shaping future recruitment strategies.
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References
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.
Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2020). ‘It's reducing a human being to a percentage’: Perceptions of justice in algorithmic decisions. CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3313831.3373620
Boell, S. K., & Cecez-Kecmanovic, D. (2020). Qualitative literature analysis: A practical approach. Communications of the Association for Information Systems, 47, 138–173.
Cowgill, B., Dell’Acqua, F., & Deng, S. (2021). Biased programmers? Or biased data? A field experiment in operationalizing AI ethics. Columbia Business School Research Paper. https://doi.org/10.2139/ssrn.3618853
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.
Jin, X., Zhang, Y., & Wang, C. (2022). Digital transformation in human resources: Challenges and strategies. Journal of Human Resource Management, 10(1), 1–10. https://doi.org/10.11648/j.jhrm.20221001.11
Köchling, A., & Wehner, M. C. (2020). Discriminated by an algorithm: A systematic review of discrimination and fairness by algorithmic decision making in the context of HR recruitment and HR development. Business Research, 13, 795–848. https://doi.org/10.1007/s40685-020-00134-w
Lal, B., & Benkraouda, O. (2025). Concept editing in AI: Mitigating bias in automated hiring systems. International Journal of AI Ethics and Practice, 3(2), 85–102.
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm based HR decision making for personal integrity. Journal of Business Ethics, 160, 377–392. https://doi.org/10.1007/s10551-019-04204-w
Mujtaba, A., & Mahapatra, B. (2024). Algorithmic injustice in digital recruitment platforms. AI and Society, 39(1), 203–216. https://doi.org/10.1007/s00146-023-01547-z
Nikolaou, I. (2021). What is the role of technology in recruitment and selection? Journal of Human Resource Management Review, 31(1), 100–108. https://doi.org/10.1016/j.hrmr.2020.100742
Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13.
Ouakili, H. (2025). Leveraging artificial intelligence for human capital efficiency: Empirical insights. Journal of Digital HR, 7(1), 45–61.
Prasanna, R., Niranjan, R., & Prakash, P. (2019). The impact of GDPR on AI enabled HRM systems. International Journal of Law and Information Technology, 27(4), 305–324.
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT), 59–68. https://doi.org/10.1145/3287560.3287598
Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.
Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a methodology for developing evidence‐informed management knowledge by means of systematic review. British Journal of Management, 14(3), 207–222.
van den Broek, E., Sergeeva, A., & Huysman, M. (2021). Hiring algorithms: An ethnographic study of fairness in practice. Journal of Strategic Information Systems, 30(4), 101667. https://doi.org/10.1016/j.jsis.2021.101667
van Esch, P., & Black, J. S. (2021). Factors that influence new generation candidates to engage with and complete digital, AI enabled recruiting. Technology Forecasting and Social Change, 169, 120822.
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2022). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 23(1), 1–24.
Vessey, I. (1991). Cognitive fit: A theory based analysis of the graphs versus tables literature. Decision Sciences, 22(2), 219–240.
Yadav, P., & Kumar, D. (2021). An empirical study on effectiveness of online recruitment. Journal of Human Capital Development, 14(2), 123–137.
Zhou, X., Yu, D., & Li, Y. (2021). The impact of AI on recruitment: Benefits and limitations. Human Resource Management Journal, 31(3), 398–412.
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