Artificial Intelligence in the Recruitment of State Civil Apparatus: A Review of the Critical Literature on Ethics and Governance
Keywords:
Artificial Intelligence; Recruitment of State Civil Apparatus; Algorithmic Justice; Governance; Public EthicsAbstract
The development of artificial intelligence (AI) has driven significant transformation in human resource management practices, including in the recruitment process in the government sector. The use of AI in the recruitment of State Civil Apparatus (ASN) is seen as able to increase the efficiency, consistency, and objectivity of the selection. However, the use of this technology also raises various normative and institutional problems, especially related to transparency, algorithmic bias, and governance. This article aims to conduct a critical literature review regarding the application of AI in ASN recruitment by emphasizing the dimensions of ethics, justice, and public governance. This study uses a critical literature review approach based on the PRISMA framework by analyzing 58 articles with national and international reputation published in the 2020-2025 period. Bibliometric analysis was carried out using VOSviewer to map the structure of knowledge, thematic clusters, and temporal development of research. The results of the analysis show that the AI discourse in public sector recruitment is evolving from an initial focus on technical efficiency to greater attention to the risk of algorithmic bias, limited transparency, and the need for accountable governance. The findings also reveal research gaps, particularly in the specific context of the State Civil Apparatus, where most of the literature is still oriented towards the private sector or general approaches. This study concludes that the integration of fair algorithm design, explainability mechanisms, and a strong governance framework is the main prerequisite for building a legitimate and equitable AI-based ASN recruitment system.
References
Albaroudi, E., Mansouri, T., & Alameer, A. (2024). Algorithmic bias in AI-based hiring systems: A systematic review. AI, 5(1), 383-404. https://doi.org/10.3390/ai5010020
Barocas, S., & Selbst, A. D. (2020). Big data’s disparate impact. California Law Review, 104(3), 671-732.
Busuioc, M. (2021). Artificial intelligence and public accountability. Public Administration Review, 81(2), 429-442. https://doi.org/10.1111/puar.13279
Cowgill, B. (2020). Bias and productivity in algorithmic hiring. Organization Science, 31(6), 1454-1474. https://doi.org/10.1287/orsc.2020.1377
Criado, J. I. (2025). Artificial intelligence and public administration: Governance and accountability challenges. Public Administration Review, 85(1), 3-16.
de Almeida, P. G. R. (2025). Understanding how public organizations implement artificial intelligence governance. Government Information Quarterly, 42(1), 101879. https://doi.org/10.1016/j.giq.2024.101879
Diakopoulos, N. (2016). Accountability in algorithmic decision making. Communications of the ACM, 59(2), 56-62. https://doi.org/10.1145/2844110
Engstrom, D. F., Ho, D. E., Sharkey, C. M., & Cuéllar, M. F. (2020). Government by algorithm: Artificial intelligence in administrative law. Administrative Law Review, 72(4), 781-836.
Fabris, A. (2025). Fairness and bias in algorithmic hiring: A multidisciplinary review. ACM Computing Surveys, 57(1), 1-38.
Hasanuddin, H., Singgarniari, E., Faisal, F., Ritonga, A., Nasution, I., Wasesa, S., & Rahayu, S. (2023). Pengaruh sarana prasarana, kualitas SDM, dan kemampuan kerja terhadap kinerja pegawai. Ekonomi, Keuangan, Investasi dan Syariah (EKUITAS), 4(2), 804-813.
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control. Academy of Management Annals, 14(1), 366-410. https://doi.org/10.5465/annals.2018.0174
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
Organisation for Economic Co-operation and Development. (2023). AI in the public sector: Opportunities and risks. OECD Publishing.
Organisation for Economic Co-operation and Development. (2024). OECD principles on artificial intelligence. OECD Publishing.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71.
Raghavan, M., Barocas, S., Kleinberg, J., & Levy, K. (2020). Mitigating bias in algorithmic hiring: Evaluating claims and practices. Proceedings of the ACM Conference on Fairness, Accountability, and Transparency, 469-481.
Rahayu, S., & Kurnianingsih, H. T. (2020). Accountability and transparency of financial reporting and acceptance of zakat. European Proceedings of Social and Behavioural Sciences.
Rigotti, C. (2024). Fairness and bias in AI applications for recruitment: A scoping review. AI & Society, 39(2), 623-640. https://doi.org/10.1007/s00146-022-01489-6
Silitonga, F. (2023). Artificial intelligence and the future of work in the Indonesian public sector. Jurnal Ilmu Sosial dan Ilmu Politik, 27(2), 145-159.
Starke, C. (2022). Fairness perceptions of algorithmic decision-making: A review of empirical evidence. Big Data & Society, 9(1), 1-14.
Wieringa, M. (2020). What to account for when accounting for algorithms: A systematic literature review on algorithmic accountability. Philosophy & Technology, 33(4), 543-572. https://doi.org/10.1007/s13347-019-00377-2
Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial intelligence and the public sector—Applications and challenges. International Journal of Public Administration, 42(7), 596-615.
Zhang, G. (2025). Explainable artificial intelligence in the talent recruitment process: A literature review. Expert Systems with Applications, 229, 120432.
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