Artificial Intelligence (AI) has been growing rapidly across industries in recent years. Not only is it increasingly in production, but it is also being introduced into the recruiting process, changing the way organizations look for and evaluate talent. Although AI has improved recruiting efficiency and effectiveness, it also raises concerns about fairness, candidate experience, and the potential replacement of human judgment.
This paper examines the differences between AI recruiting tools and traditional human resources recruiting, especially from the angle of human resources and hiring managers, to understand how they perceive the role and impact of AI in the recruiting process. The paper combines academic theories and practical experiences (especially from the semiconductor industry) and aims to fill the gap that has not been addressed in existing research, that is, how organizations can effectively integrate AI tools with human expertise in actual operations to enhance efficiency while at the same time considering fairness and fit. Current studies mainly focus on technical aspects or ethical risks, but there are still insufficient strategies and guidelines on how AI can be applied in practice and complement human judgment.
Therefore, this paper provides a combination of theoretical and practical strategies to help organizations introduce AI more effectively and locally in the human recruitment process and further consider that AI should not be a replacement for humans, but rather a tool to help humans make better decisions.

Abbott, A. (1988). The system of professions: An essay on the division of expert labor. University of Chicago Press.
Albaroudi, E., Mansouri, T., & Alameer, A. (2024). A comprehensive review of AI techniques for addressing algorithmic bias in job hiring. AI, 5(1), 383–404. https://doi.org/10.3390/ai5010019
Bar-Gil, O., Ron, T., & Czerniak, O. (2024). AI for the people? Embedding AI ethics in HR and people analytics projects. Technology in Society, 77, Article 102527. https://doi.org/10.1016/j.techsoc.2024.102527
Barley, S. R. (1989). Careers, identities, and institutions: The legacy of the Chicago School of Sociology. In M. B. Arthur, D. T. Hall, & B. S. Lawrence (Eds.), Handbook of career theory (pp. 41–65). Cambridge University Press.
Barocas, S., & Selbst, A. D. (2016). Big data’s disparate impact. California Law Review, 104(3), 671–732. https://www.jstor.org/stable/24758720
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE.
Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12. https://doi.org/10.1177/2053951715622512
Chen, Z. (2023). Ethics and discrimination in artificial intelligence-enabled recruitment practices. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02079-x
Dadheech, R., Karichalil, R. A., Sunanda, K., & Sindhu, N. (2025). AI in recruitment: Enhancing efficiency or replacing human judgement. Journal of Informatics Education and Research, 5(2), 3496–3505. https://doi.org/10.52783/jier.v5i2.2817
Horodyski, P. (2023). Applicants’ perception of artificial intelligence in the recruitment process. Computers in Human Behavior Reports, 11, 100303. https://doi.org/10.1016/j.chbr.2023.100303
Hunkenschroer, A., & Kriebitz, A. (2023). Is AI recruiting (un)ethical? A human rights perspective on the use of AI for hiring. Business Research, 16(2), 697–725. https://doi.org/10.1007/s43681-022-00166-4
Jarrahi, M. H. (2018). Artificial intelligence and the future of work: Human–AI symbiosis in organizational decision-making. Business Horizons, 61(4), 577–586. https://doi.org/10.1016/j.bushor.2018.03.007
Liem, C. C. S., Langer, M., Demetriou, A., Hiemstra, A. M. F., Wicaksana, A. S., Born, M. P., & König, C. J. (2018). Psychology meets machine learning: Interdisciplinary perspectives on algorithmic job candidate screening. In H. J. Escalante, S. Escalera, I. Guyon, X. Baró, & Y. Güçlütürk (Eds.), Explainable and interpretable models in computer vision and machine learning (pp. 197–253). Springer. https://doi.org/10.1007/978-3-319-98131-4_9
Orlikowski, W. J. (1992). The duality of technology: Rethinking the concept of technology in organizations. Organization Science, 3(3), 398–427. https://doi.org/10.1287/orsc.3.3.398
Praveen, R. V. S., Peri, S. S. S. R. G., Labde, V. V., Gudimella, A., Hundekari, S., & Shrivastava, A. (2025). AI in talent acquisition: Enhancing diversity and reducing bias. Journal of Marketing & Social Research, 2(3), 13–27. https://jmsr-online.com/article/ai-in-talent-acquisition-enhancing-diversity-and-reducing-bias-129/
Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J. F., Breazeal, C., … & Wellman, M. (2019). Machine behavior. Nature, 568(7753), 477–486. https://doi.org/10.1038/s41586-019-1138-y
Rao, S., & Zhao, T. (2025). Ethical AI in HR: A case study of tech hiring. Journal of Computer Information Systems. Advance online publication. https://www.tandfonline.com/doi/full/10.1080/08874417.2024.2446954
Seppälä, P., & Małecka, M. (2024). AI and discriminative decisions in recruitment: Challenging the core assumptions. Big Data & Society, 11(1), 1–20. https://doi.org/10.1177/20539517241235872
Tambe, P., Cappelli, P., & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910