ALGORITHMIC TRANSPARENCY IN DIGITAL PUBLIC SERVICES: AN ADMINISTRATIVE LAW PERSPECTIVE

Authors

  • Abdul Wahab Institut Pemerintahan Dalam Negeri
  • Muhammad Suhardi Institut Pemerintahan Dalam Negeri

DOI:

https://doi.org/10.69916/jkbti.v5i1.461

Keywords:

administrative law, algorithmic transparency, artificial intelligence, digital public services, right to explanation

Abstract

The increasing use of artificial intelligence and automated decision-making systems in digital public services has created new challenges for administrative law, particularly regarding transparency, accountability, and citizens’ procedural rights. This study examines algorithmic transparency as a legal obligation of government institutions in AI-based public service delivery. Using a normative juridical method with statutory, conceptual, and comparative approaches, this article analyses how the right to explanation can be constructed as part of administrative due process, reason-giving, and good administration. The findings show that the use of algorithmic systems does not reduce the government’s responsibility to provide lawful, reasonable, and reviewable decisions. Instead, the complexity of AI-based decision-making strengthens the need for meaningful explanations that are understandable, case-relevant, and useful for citizens affected by public decisions. This study argues that the right to explanation should not be limited to technical disclosure of algorithmic models, but should include information on whether AI was used, how it influenced the decision, what data and criteria were considered, and what remedies are available. The novelty of this article lies in positioning algorithmic transparency within the doctrinal framework of administrative law, rather than treating it solely as an ethical or technological issue. The study contributes to the development of accountable, citizen-centred, and legally grounded AI governance in digital public administration.

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References

N. Aoki, T. Tatsumi, G. Naruse, and K. Maeda, “Explainable AI for government: Does the type of explanation matter to the accuracy, fairness, and trustworthiness of an algorithmic decision as perceived by those who are affected?,” Government Information Quarterly, vol. 41, no. 4, 2024, Art. no. 101965, doi: https://doi.org/10.1016/j.giq.2024.101965.

A. Rizk and I. Lindgren, “Automated decision-making in public administration: Changing the decision space between public officials and citizens,” Government Information Quarterly, vol. 42, no. 3, 2025, Art. no. 102061, doi: https://doi.org/10.1016/j.giq.2025.102061.

T. S. Gesk and M. Leyer, “Artificial intelligence in public services: When and why citizens accept its usage,” Government Information Quarterly, vol. 39, no. 3, 2022, Art. no. 101704, doi: https://doi.org/10.1016/j.giq.2022.101704.

H. de Bruijn, M. Warnier, and M. Janssen, “The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making,” Government Information Quarterly, vol. 39, no. 2, 2022, Art. no. 101666, doi: https://doi.org/10.1016/j.giq.2021.101666.

S. Grimmelikhuijsen, “Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision-making,” Public Administration Review, vol. 83, no. 2, pp. 241–262, 2023, doi: https://doi.org/10.1111/puar.13483.

M. J. Ahn and Y.-C. Chen, “Digital transformation toward AI-augmented public administration: The perception of government employees and the willingness to use AI in government,” Government Information Quarterly, vol. 39, no. 2, 2022, Art. no. 101664, doi: https://doi.org/10.1016/j.giq.2021.101664.

C. van Noordt and G. Misuraca, “Artificial intelligence for the public sector: Results of landscaping the use of AI in government across the European Union,” Government Information Quarterly, vol. 39, no. 3, 2022, Art. no. 101714, doi: https://doi.org/10.1016/j.giq.2022.101714.

F. Selten and B. Klievink, “Organizing public sector AI adoption: Navigating between separation and integration,” Government Information Quarterly, vol. 41, no. 1, 2024, Art. no. 101885, doi: https://doi.org/10.1016/j.giq.2023.101885.

C. van Noordt, R. Medaglia, and L. Tangi, “Policy initiatives for artificial intelligence-enabled government: An analysis of national strategies in Europe,” Public Policy and Administration, vol. 40, no. 2, pp. 215–253, 2025, doi: https://doi.org/10.1177/09520767231198411.

A. Meijer, L. Lorenz, and M. Wessels, “Algorithmization of bureaucratic organizations: Using a practice lens to study how context shapes predictive policing systems,” Public Administration Review, vol. 81, no. 5, pp. 837–846, 2021, doi: https://doi.org/10.1111/puar.13391.

R. Williams, “Rethinking administrative law for algorithmic decision making,” Oxford Journal of Legal Studies, vol. 42, no. 2, pp. 468–494, 2022, doi: https://doi.org/10.1093/ojls/gqab032.

J. C. Covilla, “Artificial intelligence and administrative discretion: Exploring adaptations and boundaries,” European Journal of Risk Regulation, vol. 16, no. 1, pp. 36–50, 2025, doi: https://doi.org/10.1017/err.2024.76.

G. Rudolf and P. Kovač, “The role of automated decision-making in modern administrative law: Challenges and data protection implications,” Central European Public Administration Review, vol. 22, no. 2, pp. 83–108, 2024, doi: https://doi.org/10.17573/cepar.2024.2.04.

T. W. Kim and B. R. Routledge, “Why a right to an explanation of algorithmic decision-making should exist: A trust-based approach,” Business Ethics Quarterly, vol. 32, no. 1, pp. 75–102, 2022, doi: https://doi.org/10.1017/beq.2021.3.

K. Vredenburgh, “The right to explanation,” Journal of Political Philosophy, vol. 30, no. 2, pp. 209–229, 2022, doi: https://doi.org/10.1111/jopp.12262.

E. Taylor, “Explanation and the right to explanation,” Journal of the American Philosophical Association, vol. 10, no. 3, pp. 467–482, 2024, doi: https://doi.org/10.1017/apa.2023.7.

L. A. Munch, J. C. Bjerring, and J. T. Mainz, “Algorithmic decision-making: The right to explanation and the significance of stakes,” Big Data & Society, vol. 11, no. 1, 2024, doi: https://doi.org/10.1177/20539517231222872.

W. J. von Eschenbach, “Transparency and the black box problem: Why we do not trust AI,” Philosophy & Technology, vol. 34, no. 4, pp. 1607–1622, 2021, doi: https://doi.org/10.1007/s13347-021-00477-0.

M. Günther and A. Kasirzadeh, “Algorithmic and human decision making: For a double standard of transparency,” AI & Society, vol. 37, no. 1, pp. 375–381, 2022, doi: https://doi.org/10.1007/s00146-021-01200-5.

F. Jongepier and E. Keymolen, “Explanation and agency: Exploring the normative-epistemic landscape of the ‘right to explanation’,” Ethics and Information Technology, vol. 24, no. 4, Art. no. 49, 2022, doi: https://doi.org/10.1007/s10676-022-09654-x.

D. G. Grant, J. Behrends, and J. Basl, “What we owe to decision-subjects: Beyond transparency and explanation in automated decision-making,” Philosophical Studies, vol. 182, pp. 55–85, 2025, doi: https://doi.org/10.1007/s11098-023-02013-6.

T. Haesevoets, B. Verschuere, R. Van Severen, and A. Roets, “How do citizens perceive the use of artificial intelligence in public sector decisions?,” Government Information Quarterly, vol. 41, no. 1, 2024, Art. no. 101906, doi: https://doi.org/10.1016/j.giq.2023.101906.

P. Mikalef, K. Lemmer, C. Schaefer, M. Ylinen, S. O. Fjørtoft, H. Y. Torvatn, M. Gupta, and B. Niehaves, “Enabling AI capabilities in government agencies: A study of determinants for European municipalities,” Government Information Quarterly, vol. 39, no. 4, 2022, Art. no. 101596, doi: https://doi.org/10.1016/j.giq.2021.101596.

J. I. Criado and L. O. de Zarate-Alcarazo, “Technological frames, CIOs, and artificial intelligence in public administration: A socio-cognitive exploratory study in Spanish local governments,” Government Information Quarterly, vol. 39, no. 4, 2022, Art. no. 101688, doi: https://doi.org/10.1016/j.giq.2022.101688.

T. Haesevoets, B. Verschuere, and A. Roets, “AI adoption in public administration: Perspectives of public sector managers and public sector non-managerial employees,” Government Information Quarterly, vol. 42, no. 2, 2025, Art. no. 102029, doi: https://doi.org/10.1016/j.giq.2025.102029.

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Published

2026-01-23

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How to Cite

[1]
Abdul Wahab and Muhammad Suhardi, “ALGORITHMIC TRANSPARENCY IN DIGITAL PUBLIC SERVICES: AN ADMINISTRATIVE LAW PERSPECTIVE”, JKBTI, vol. 5, no. 1, pp. 93–100, Jan. 2026.