ADOPTION OF AI IN RECRUITMENT: IMPLICATIONS FOR BIAS, EFFICIENCY, AND CANDIDATE EXPERIENCE

Authors

  • Bungaran Panggabean Mulia Pratama College of Economic, Bekasi
  • Welis Raldianingrat Universitas Lakidende
  • Baharudin Baharudin Universitas Lakidende
  • Irham Natsir K STIM LPI Makassar

DOI:

https://doi.org/10.31539/m386pt53

Keywords:

Adoption, AI, Recruitment

Abstract

However, while the integration of AI into recruitment practices offers numerous potential benefits, it also raises critical concerns regarding fairness, transparency, and candidate experience. A central issue is the risk of algorithmic bias. The purpose of this study is to analyze the adoption of Artificial Intelligence (AI) in the recruitment process: its implications for bias, efficiency, and candidate experience. This study employs a literature review methodology to systematically analyze and synthesize existing research on the adoption of Artificial Intelligence (AI) in recruitment, with a particular focus on three interrelated aspects: bias, efficiency, and candidate experience. This literature review explored the multifaceted implications of Artificial Intelligence (AI) adoption in recruitment, focusing on three key dimensions: bias, efficiency, and candidate experience. The findings reveal that while AI technologies offer substantial benefits in terms of operational speed and cost reduction, they also introduce significant ethical, social, and psychological challenges.

References

Ajunwa, I. (2020). The paradox of automation as anti-bias intervention. Cardozo Law Review, 41(5), 1671–1734. https://doi.org/10.2139/ssrn.2741306

Binns, R., Veale, M., Van Kleek, M., & Shadbolt, N. (2018). ‘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/10.1145/3173574.3173951

Black, J. S., & van Esch, P. (2020). AI-enabled recruitment: What is it and how should a manager use it? Business Horizons, 63(2), 215–226. https://doi.org/10.1016/j.bushor.2019.12.001

Chamorro-Premuzic, T., Winsborough, D., Sherman, R. A., & Hogan, R. (2016). New talent signals: Shiny new objects or a brave new world? Industrial and Organizational Psychology, 9(3), 621–640. https://doi.org/10.1017/iop.2016.6

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.3693916

Dastin, J. (2018, October 10). Amazon scraps secret AI recruiting tool that showed bias against women. Reuters. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight-idUSKCN1MK08G

Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint. https://arxiv.org/abs/1702.08608

European Commission. (2021). Proposal for a regulation laying down harmonised rules on artificial intelligence (Artificial Intelligence Act). https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206

Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35. https://doi.org/10.1145/3457607

Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 469–481. https://doi.org/10.1145/3351095.3372828

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339. https://doi.org/10.1016/j.jbusres.2019.07.039

Strohmeier, S., & Piazza, F. (2015). Artificial intelligence in human resource management: A review of empirical research. International Journal of Human Resource Management, 30(8), 1251–1280. https://doi.org/10.1080/09585192.2016.1254100

Suen, H. Y., Chen, M. Y. C., & Lu, S. H. (2019). Does the use of intelligent interviewing systems in HRM reduce bias? Computers in Human Behavior, 98, 93–101. https://doi.org/10.1016/j.chb.2019.04.012

Upadhyay, A. K., & Khandelwal, K. (2018). Applying artificial intelligence: Implications for recruitment. Strategic HR Review, 17(5), 255–258. https://doi.org/10.1108/SHR-07-2018-0052

Van Esch, P., & Black, J. S. (2019). Factors that influence new generation candidates to engage with AI-enabled recruitment. Journal of Recruitment Science, 2(1), 1–15.

Whittemore, R., & Knafl, K. (2005). The integrative review: Updated methodology. Journal of Advanced Nursing, 52(5), 546–553. https://doi.org/10.1111/j.1365-2648.2005.03621.x

Wirtky, T., Berente, N., & Thatcher, S. M. B. (2023). Ethics of artificial intelligence in human resource management: A review and research agenda. Journal of Business Ethics, 188, 353–375. https://doi.org/10.1007/s10551-021-04917-3

Zhou, S., & Goh, Y. M. (2021). Chatbot-based applicant screening in human resource management: A review. Technological Forecasting and Social Change, 169, 120837. https://doi.org/10.1016/j.techfore.2021.120837

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Published

2025-07-21

Issue

Vol. 8 No. 4 (2025): COSTING : Journal of Economic, Bussines and Accounting

Section

Articles

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Copyright (c) 2025 Bungaran Panggabean, Welis Raldianingrat, Baharudin Baharudin, Irham Natsir K

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