Artificial intelligence dalam Rekrutmen dan Seleksi Karyawan : Manfaat dan Tantangannya
DOI:
https://doi.org/10.55606/jupumi.v3i3.3685Keywords:
Artificial Intelligence, Recruitment, Employee Selection, Algorithmic Bias, EfficiencyAbstract
Artificial intelligence (AI) is revolutionizing recruitment by automating candidate screening, interview analysis, and job success prediction. This study evaluates the benefits and challenges of implementing AI in employee selection through case studies and comparative analysis. The results show that AI improves the efficiency and objectivity of selection, but faces obstacles such as algorithmic bias, system transparency, and user resistance. To overcome these challenges, recommended solutions include bias mitigation strategies, integration of AI with human evaluation, and implementation of best practices. This study provides insights for companies and Human Resource (HR) practitioners in optimizing AI for more effective and fair employee selection.
References
Anwar, K., Romli, O., Salapudin, S., Pratiwi, I., Asfar, A. H., Fatari, & Quraysin, I. (2023). Transformasi digital dalam manajemen sumber daya manusia: Dampak dan tantangannya.
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy.
Binns, R. (2021). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society.
Eubanks, B. (2018). Artificial intelligence for HR: Use AI to support and develop a successful workforce. Kogan Page Publishers.
Gartner. (2022). The impact of AI on recruitment: Efficiency, effectiveness, and ethical considerations. Gartner Industry Research Report.
Harvard Business Review. (2021). How AI can reduce—but also reinforce—bias in hiring. Harvard Business Review. Diakses dari https://hbr.org
LinkedIn. (2023). [Judul artikel jika ada]. Diakses dari https://www.linkedin.com
McKinsey & Company. (2023). The state of AI in human resource management. Diambil dari https://www.mckinsey.com
Nasution, A. F. (2023). Metode penelitian kualitatif.
Nguyen, A., & Brusoni, S. (2020). Artificial intelligence in hiring: The impact on recruitment efficiency and fairness. Journal of Business Ethics, 167(4), 767–780. https://doi.org/10.1007/s10551-019-04376-6
O'Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing.
Raji, I. D., & Buolamwini, J. (2019). Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial AI systems. In Proceedings of the AAAI/ACM Conference on AI Ethics and Society.
Sakinah, R., & Kuswinarno, M. (2024). Dampak kecerdasan buatan terhadap digitalisasi dan kinerja sumber daya manusia: Peluang dan tantangannya. Jurnal Media Akademik (JMA), 2(9).
Schmidt, G. B., & Hunter, J. E. (2016). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings. Psychological Bulletin, 124(2), 262–274. https://doi.org/10.1037/a0039085
Sewang, S. (2023). Pengaruh budaya organisasi dan gaya kepemimpinan terhadap kepuasan kerja pegawai Bappeda Kabupaten Jeneponto. Public Service and Governance Journal, 4(2), 51–60.
Sewang, S. (2023). Pengaruh motivasi terhadap kinerja pegawai Dinas Kelautan dan Perikanan Kabupaten Takalar. Economics and Digital Business Review, 4(1), 109–199.
Wilson, H. J., Daugherty, P. R., & Bianzino, N. (2021). Artificial intelligence and the future of recruitment. MIT Sloan Management Review.
Ziebart, B. D., Maas, A. L., & Williamson, D. P. (2020). Ethics and fairness in AI hiring systems: Managing the risks of algorithmic bias in recruitment. In Proceedings of the Conference on Fairness, Accountability, and Transparency (FAT).
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