Reconciling Administrative Efficiency with Procedural Safeguards in the ADM System
DOI:
https://doi.org/10.46282/blr.2025.9.2.1116Keywords:
Reconciling, Administrative Efficiency, Procedural Safeguards, Automated Decision-Making, Artificial IntelligenceAbstract
The use of automated systems for making administrative decisions has recently increased to improve efficiency and speed in completing tasks. However, ensuring that administrative effectiveness aligns with procedural safeguards when using an Automated Decision-Making (ADM) system is crucial to maintaining adequate performance and ensuring that ADM use remains safe for individuals. Using a descriptive methodology, the article collected and analysed data on striking a reconcile between administrative efficiency and procedural protections in the ADM system to present a precise and straightforward overview of current literature. Sources for data included books, reports, conferences, conventions, and internet resources. The article revealed several key mechanisms that help reconcile administrative efficiency with procedural safeguards in the use of ADM for administrative tasks. These include compliance with the law, ensuring fairness, exercising rights freely, pursuing justice, adding human oversight, avoiding decisions made solely by automated systems, ensuring transparency, explainability, and interpretability of ADM-based decisions, and maintaining accountability. The analysis suggested that future investigations should examine how core elements of administrative decisions, such as jurisdiction, form, and purpose, are influenced by ADM, and should also evaluate the principles of reason-giving and nondelegation of power.
References
Ailyn, D. (2024). Explainability and Interpretability in Deep Learning Models. Available at: https://www.researchgate.net/publication/383907231_Explainability_and_Interpretability_in_Deep_Learning_Models (accessed on 25.10.2025).
Allars, M. (2024). Automated Decision-Making and Review of Administrative Decisions. Georgia Law Review, 58(3), 1145-1182, https://digitalcommons.law.uga.edu/glr/vol58/iss3/8
Aysolmaz, B., Müller, R. and Meacham, D. (2023). The public perceptions of algorithmic decision-making systems: Results from a large-scale survey. Telematics and Informatics, 79, 101954. https://doi.org/10.1016/j.tele.2023.101954 DOI: https://doi.org/10.1016/j.tele.2023.101954
Barbera, E. and D’Ottavio, A. (2025). CJEU decision on algorithmic transparency and secret protection (C-203/22). Bird & Bird, published on 29 April 2025. Available at: https://www.twobirds.com/en/insights/2025/cjeu-decision-on-algorithmic-transparency-and-secret-protection-%28cjeu-c-20322%29 (accessed on 02.10.2025.
Bernardo, V. and Hernández, L. (2025). European Data Protection Supervisor.TechDispatch #2/2025: Human oversight of automated decision-making. Available at: https://www.edps.europa.eu/system/files/2025-09/25-09-15_techdispatch-human-oversight_en.pdf (accessed on 10.10.2025).
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 DOI: https://doi.org/10.1177/2053951715622512
Cobbe, J., Lee, M. S. A. and Singh, J. (2021). Reviewable Automated Decision-Making: A Framework for Accountable Algorithmic Systems. In Conference on Fairness, Accountability, and Transparency (FAccT’21), March 3–10, 2021, Virtual Event, Canada, 598-609. https://doi.org/10.1145/3442188.3445921 DOI: https://doi.org/10.1145/3442188.3445921
Commonwealth Ombudsman (2025). Automated Decision-making – Better Practice Guide, March 2025. Available at: https://www.ombudsman.gov.au/__data/assets/pdf_file/0025/317437/Automated-Decision-Making-Better-Practice-Guide-March-2025.pdf (accessed on 28.10.2025).
Decker, M., Wegner, L. and Leicht-Scholten, C. (2024). Procedural fairness in algorithmic decision-making: the role of public engagement. Ethics and Information Technology, 27(1), 1–16. https://doi.org/10.1007/s10676-024-09811-4 DOI: https://doi.org/10.1007/s10676-024-09811-4
Finck, M. (2019). Automated Decision-Making and Administrative Law. Forthcoming, P. Cane et al. (eds), Oxford Handbook of Comparative Administrative Law. Oxford: Oxford University Press, Max Planck Institute for Innovation & Competition Research Paper No. 19-10, 25 pp. https://ssrn.com/abstract=3433684
Fink, M. (2025). Human Oversight under Article 14 of the EU AI Act. Available at: http://dx.doi.org/10.2139/ssrn.5147196 (accessed on 25.10.2025). DOI: https://doi.org/10.2139/ssrn.5146118
Frasca, M., La Torre, D., Pravettoni, G. and Cutica, I. (2024). Explainable and interpretable artificial intelligence in medicine: A systematic bibliometric review. Discover Artificial Intelligence, 4, 15. https://doi.org/10.1007/s44163-024-00114-7. DOI: https://doi.org/10.1007/s44163-024-00114-7
Green, B. (2022). The flaws of policies requiring human oversight of government algorithms. Computer Law & Security Review, 45, 105681. https://doi.org/10.1016/j.clsr.2022.105681 DOI: https://doi.org/10.1016/j.clsr.2022.105681
Henning, A., and Langenbach, P. (2025). Bridging the human-AI fairness gap: How providing reasons enhances the perceived fairness of public decision-making (SSRN Scholarly Paper No. 4819145). https://doi.org/10.2139/ssrn.4819145 DOI: https://doi.org/10.2139/ssrn.4819145
Hirvonen, H. (2024). Just accountability structures – a way to promote the safe use of automated decision-making in the public sector. AI & Soc, 39, 155–167, https://doi.org/10.1007/s00146-023-01731-z DOI: https://doi.org/10.1007/s00146-023-01731-z
Hofmann, H., C. H. and Pflücke, F. (2024). Governance of Automated Decision-Making and EU Law. Oxford: Oxford University Press. https://library.oapen.org/handle/20.500.12657/93892 DOI: https://doi.org/10.1093/9780198919575.001.0001
Hubková, P. (2024). EU administrative decision-making delegated to machines: Legal challenges and issues. Acta Universitatis Carolinae Iuridica, 70(2), 101-119, https://doi.org/10.14712/23366478.2024.25 DOI: https://doi.org/10.14712/23366478.2024.25
Huggins, A. (2021). Addressing Disconnection: Automated Decision-Making, Administrative Law, and Regulatory Reform. UNSW Law Journal, 44(3), 1048–1077. https://doi.org/10.53637/WCGG2401 DOI: https://doi.org/10.53637/WCGG2401
Idowu, S. O., Capaldi, N., Zu, L. and Gupta, A.D. (2013). Legal Compliance. In: Idowu, S.O., Capaldi, N., Zu, L. and Gupta, A.D. (eds.), Encyclopedia of Corporate Social Responsibility. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-642-28036-8_100980 DOI: https://doi.org/10.1007/978-3-642-28036-8
Iwan, D. (2021). Applicability of Human Rights Control Mechanisms in Algorithmic Decision-making Cases. Revista Direitos Fundamentais & Democracia, 26(2), 269–291. https://doi.org/10.25192/issn.1982-0496.rdfd.v26i22286 DOI: https://doi.org/10.25192/issn.1982-0496.rdfd.v26i22286
Jaworski, M. V., Patel, C. H., and Bloom, A. (2025). How to explain automated decisions: Recent CJEU decision and CPPA rulemaking offer insight into ADM explainability. Clark Hill PLC. Available at: https://www.clarkhill.com/news-events/news/how-to-explain-automated-decisions-recent-cjeu-decision-and-cppa-rulemaking-offer-insight-into-adm-explainability/ (accessed on 29.10.2025).
Kaun, A. (2021). Suing the algorithm: the mundanization of automated decision-making in public services through litigation. Information, Communication & Society, 25(14), 2046–2062, https://doi.org/10.1080/1369118X.2021.1924827 DOI: https://doi.org/10.1080/1369118X.2021.1924827
Malgieri, G. (2019). Automated decision-making in the EU Member States: The right to explanation and other “suitable safeguards” in the national legislations. Computer Law & Security Review, 35(5), 105327, 1-26, https://doi.org/10.1016/j.clsr.2019.05.002 DOI: https://doi.org/10.1016/j.clsr.2019.05.002
Maslej, N. et al. (2025). AI Index 2025 Annual Report. Stanford University, Institute for Human-Centered Artificial Intelligence. Available at: https://hai.stanford.edu/ai-index/2025-ai-index-report (accessed on 12.12.2024)
McCabe, B. (2020). Automated decision-making in (good) government. Aial Forum, 106–129. Available at: http://classic.austlii.edu.au/au/journals/AIAdminLawF/2020/24.pdf (accessed on 29.10.2025).
Mokander, J., Morley, J., Taddeo, M., and Floridi, L. (2021). Ethics-Based Auditing of Automated Decision-Making Systems: Nature, Scope, and Limitations. Sci Eng Ethics, 27(44). https://doi.org/10.1007/s11948-021-00319-4 DOI: https://doi.org/10.1007/s11948-021-00319-4
Nešpor, J. (2024). Automated Administrative Decision-Making: What is the Black?, Acta Universitatis Carolinae – Iuridica, 70(2), 69–83, DOI: 10.14712/23366478.2024.23 DOI: https://doi.org/10.14712/23366478.2024.23
Ng, Y.-F., O’Sullivan, M., Paterson, M., and Witzleb, N. (2020). Revitalising public law in a technological era: Rights, transparency and administrative justice. University of New South Wales Law Journal, 43(3), 1041–1077, https://doi.org/10.53637/YGTS5583 DOI: https://doi.org/10.53637/YGTS5583
Parycek, P., Schmid, V., and Novak, AS. (2023). Artificial Intelligence (AI) and Automation in Administrative Procedures: Potentials, Limitations, and Framework Conditions. Journal of the Knowledge Economy, 15, 8390–8415, https://doi.org/10.1007/s13132-023-01433-3 DOI: https://doi.org/10.1007/s13132-023-01433-3
Rizk, A. and Lindgren, I. (2025). Automated decision-making in public administration: Changing the decision space between public officials and citizens. Government Information Quarterly, 42(3), 1-14, https://doi.org/10.1016/j.giq.2025.102061 DOI: https://doi.org/10.1016/j.giq.2025.102061
Roehl, U. B. U. and Hansen, M. B. (2024). Automated administrative decision-making and good governance: Synergies, trade-offs, and limits. Public Administration Review, 84(6), 1184–1199, DOI: 10.1111/puar.13799. DOI: https://doi.org/10.1111/puar.13799
Russell, S., J. and Norvig, P. (2010). Artificial intelligence: A modern approach (3rd ed.). Upper Sadle River: Prentice Hall.
Sales, P. (2024). Judicial review methodology in the automated state (Speech delivered at the Conference on Automation in Public Governance, Prato, Italy). The Supreme Court of the United Kingdom. https://supremecourt.uk/uploads/speech_lord_sales_2409_157955367d.pdf
Schoeffer, J. (2022). A human-centric perspective on fairness and transparency in algorithmic decision-making. arXiv. https://doi.org/10.48550/arXiv.2205.00033 DOI: https://doi.org/10.1145/3491101.3503811
Shah, H. (2018). Algorithmic accountability. Philosophical Transactions of the Royal Society, 376(2128). https://doi.org/10.1098/rsta.2017.0362 DOI: https://doi.org/10.1098/rsta.2017.0362
Starke, C., Baleis, J., Keller, B. and Marcinkowski, F. (2022). Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature. Big Data & Society, 9(2). DOI: 10.1177/20539517221115189 DOI: https://doi.org/10.1177/20539517221115189
Suksi, M. (2021). Administrative due process when using automated decision-making in public administration: Some notes from a Finnish perspective. Artificial Intelligence and Law, 29(1), 87–110, https://doi.org/10.1007/s10506-020-09269-x DOI: https://doi.org/10.1007/s10506-020-09269-x
Szafran, D. and Bach, R. (2024). The Human Must Remain the Central Focus: Subjective Fairness Perceptions in Automated Decision-Making. Minds & Machines, 34(24), 1–37. https://doi.org/10.1007/s11023-024-09684-y DOI: https://doi.org/10.1007/s11023-024-09684-y
Veale, M., Van Kleek, M. and Binns, R. (2018). Fairness and accountability design needs for algorithmic support in high-stakes public sector decision-making. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3173574.3174014 DOI: https://doi.org/10.1145/3173574.3174014
Wihlborg, E., Larsson, H. and Hedström, K. (2016). The computer says no! – A case study on automated decision-making in public authorities. 2016 49th Hawaii International Conference on System Sciences (HICSS), 2903–2912. IEEE. https://doi.org/10.1109/HICSS.2016.364 DOI: https://doi.org/10.1109/HICSS.2016.364
Williams, R. A. (2021). Rethinking administrative law for algorithmic decision making. Oxford Journal of Legal Studies, 42(2), 468–494. https://doi.org/10.1093/ojls/gqab032 DOI: https://doi.org/10.1093/ojls/gqab032
Zalnieriute, M., Moses, L. B. and Williams, G. (2019). The Rule of Law and Automation of Government Decision-Making. Modern Law Review, 82(3), 1–28, https://dx.doi.org/10.2139/ssrn.3348831 DOI: https://doi.org/10.1111/1468-2230.12412
Zhyvko, M., Dombrovska, A. and Kiblyk, D. (2025). The Role of Ombudsman Institutions in Administrative Accountability: A Comparative Perspective. Public Administration and Law Review, 1(21), 87–98, https://doi.org/10.36690/2674- 5216-2025-1-87-98 DOI: https://doi.org/10.36690/2674-5216-2025-1-87-98
Zou, C. X. and Zhang, F. Y. (2022). Algorithm Interpretation Right—The First Step to Algorithmic Governance. Beijing Law Review, 13, 227–246, https://doi.org/10.4236/blr.2022.132015 DOI: https://doi.org/10.4236/blr.2022.132015
Directive on Automated Decision-Making (2019). Available at: https://www.tbs-sct.canada.ca/pol/doc-eng.aspx?id=32592 (accessed on 12.10.2025).
District Court of The Hague. (2020). NJCM et al. v. The Netherlands (SyRI – System Risk Indication), ECLI:NL: RBDHA:2020:1878. Available at: from https://www.escr-net.org/wp-content/uploads/2020/09/ecli_nl_rbdha_2020_1878.pdf (accessed on 29.10.2025).
European Data Protection Board (2017). Guidelines on Automated individual decision-making and Profiling for the purposes of Regulation 2016/679. Available at: https://ec.europa.eu/newsroom/article29/items/612053/en (accessed on 29.10.2025).
European Law Institute (2022). Guiding Principles for Automated Decision-Making in the EU. Available at: https://www.europeanlawinstitute.eu/fileadmin/user_upload/p_eli/Publications/ELI_Innovation_Paper_on_Guiding_Principles_for_ADM_in_the_EU.pdf (accessed on 24.10.2025).
European Law Institute (2023). EU Consumer Law and Automated Decision-Making (ADM): Is EU Consumer Law Ready for ADM?. Available at: https://www.europeanlawinstitute.eu/fileadmin/user_upload/p_eli/Publications/ELI_Interim_Report_on_EU_Consumer_Law_and_Automated_Decision-Making.pdf (accessed on 25.10.2025).
Future of Privacy Forum. (2022). Automated decision-making under the GDPR: Practical cases from courts and data protection authorities (Report). Available at: https://fpf.org/wp-content/uploads/2022/05/FPF-ADM-Report-R2-singles.pdf (accessed on 24.10.2025).
Law Council of Australia (2025). Use of Automated Decision-Making by Government: Consultation Paper, 24 January 2025. Available at: https://lawcouncil.au/publicassets/be8251b1-d4dd-ef11-94af-005056be13b5/4643%20-%20S%20-%20Use%20of%20automated%20decision-making%20by%20government.pdf (accessed on 12.10.2025).
NSW Ombudsman (2021). The new machinery of government: Using machine technology in administrative decision-making, A special report under section 31 of the Ombudsman Act 1974. Available at: https://cmsassets.ombo.nsw.gov.au/assets/Reports/The-new-machinery-of-government-special-report_Front-section.pdf (accessed on 24.10.2025).
NSW Ombudsman (2023). „Safe and Responsible AI in Australia“ discussion paper. Available at: https://consult.industry.gov.au/supporting-responsible-ai/submission/view/357 (accessed on 29.10.2025).
Public Administration Institute (n.d.). Understanding the Concept and Significance of Accountability. PubAdmin.Institute, published on February 9, 2025. Available at: https://pubadmin.institute/public-systems-management/understanding-significance-accountability (accessed on 17.12.2025).
Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation).
Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence and amending Regulations (EC) No 300/2008, (EU) No 167/2013, (EU) No 168/2013, (EU) 2018/858, (EU) 2018/1139 and (EU) 2019/2144 and Directives 2014/90/EU, (EU) 2016/797 and (EU) 2020/1828 (Artificial Intelligence Act).
The Royal Society (2019). Explainable AI: the basics: Policy Briefing, November 2019. Available at: https://ec.europa.eu/futurium/en/system/files/ged/ai-and-interpretability-policy-briefing_creative_commons.pdf (accessed on 30.10.2025).
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