Harmonizing the Principles of the EU AI Act with Indonesian AUPB for Transparent Algorithmic Governance
DOI:
https://doi.org/10.59261/jlsp.v4i5.291Keywords:
algorithmic discretion, right to explanation, Algorithmic Impact Assessment, Asas-Asas Umum Pemerintahan yang Baik (AUPB)Abstract
Background: The digitalization of government procurement through Indonesia’s E-Catalog system has shifted certain functions traditionally performed by human bureaucrats to algorithmic systems that exercise what may be termed “algorithmic discretion” in product ranking and recommendation. Legal challenges arise because Indonesia’s administrative law framework, particularly Law No. 30 of 2014 on Government Administration, remains largely anthropocentric and inadequately addresses algorithmic systems acting as “invisible bureaucrats.” This creates an accountability gap when algorithmic decisions produce hidden biases that adversely affect providers’ rights.
Objective: This research aims to harmonize the right-to-explanation principle under the EU AI Act and Canada’s Algorithmic Impact Assessment (AIA) framework with Indonesia’s General Principles of Good Governance (Asas-Asas Umum Pemerintahan yang Baik, AUPB) to formulate a doctrine of “Administrative Due Process for Algorithms.”
Methods: This research employs comparative legal analysis to examine the Canadian AIA framework and the EU AI Act in relation to Indonesia’s administrative law framework.
Results: The findings indicate that the AUPB principles of diligence (kecermatan) and openness (keterbukaan) can be operationalized as obligations to audit black-box algorithms, giving rise to a new theoretical construct termed “Algorithmic Administrative Law.” This approach shifts the focus of administrative oversight from the examination of physical documents to the auditing of algorithmic logic.
Conclusion: This research recommends amending Law No. 30 of 2014 on Government Administration to explicitly address decisions made through automated decision-making systems, as well as adopting an AIA protocol through Indonesia’s National Public Procurement Agency (Lembaga Kebijakan Pengadaan Barang/Jasa Pemerintah, LKPP) as a mandatory audit mechanism before algorithmic ranking systems are deployed for public use.
References
Agustianto, A., Budi, H., Sudirman, L., Nurlaily, N., & Afdal, W. (2025). Analisis risiko finansial perbankan melalui artificial intelligence (AI): Politik hukum dan potensi pengembangan hukum. Jurnal Magister Hukum Udayana (Udayana Master Law Journal). https://doi.org/10.24843/jmhu.2025.v14.i01.p02
Ardiansyah, Kristiane, D., & Nurkhaerah, S. (2025). Analisis pemanfaatan artificial intelligence dalam pelayanan publik menurut prinsip fikih siy?sah. Qaumiyyah: Jurnal Hukum Tata Negara. https://doi.org/10.24239/qaumiyyah.v6i1.206
Armiwulan, H., Rahman, R., Prabowo, V., & Hajdú, J. (2024). Artificial intelligence and its challenges to elections in Indonesia: A legal analysis. Jambura Law Review. https://doi.org/10.33756/jlr.v6i2.24243
Arora, C., & Sarkar, D. (2023). Auditing artificial intelligence as a new layer of mediation: Introduction of a new black box to address another black box. Hipertext.net. https://doi.org/10.31009/hipertext.net.2023.i26.10
Arrieta, A., Rodríguez, N., Ser, J., Bennetot, A., Tabik, S., Barbado, A., García, S., Gil-Lopez, S., Molina, D., Benjamins, R., Chatila, R., & Herrera, F. (2019). Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Information Fusion, 58, 82–115. https://doi.org/10.1016/j.inffus.2019.12.012
Bahram, M. (2025). Reorientasi peran hukum dalam menghadapi disrupsi teknologi: Studi normatif tentang regulasi digital di Indonesia. SINERGI: Jurnal Riset Ilmiah. https://doi.org/10.62335/sinergi.v2i3.1078
Bayaml?o?lu, E. (2021). The right to contest automated decisions under the General Data Protection Regulation: Beyond the so?called “right to explanation”. Regulation & Governance. https://doi.org/10.1111/rego.12391
Brkan, M. (2019). Do algorithms rule the world? Algorithmic decision-making and data protection in the framework of the GDPR and beyond. Int. J. Law Inf. Technol., 27, 91–121. https://doi.org/10.1093/ijlit/eay017
Brown, S., Davidovi?, J., & Hasan, A. (2021). The algorithm audit: Scoring the algorithms that score us. Big Data & Society, 8. https://doi.org/10.1177/2053951720983865
Casey, B., Farhangi, A., & Vogl, R. (2018). Rethinking explainable machines: The GDPR's 'right to explanation' debate and the rise of algorithmic audits in enterprise. Berkeley Technology Law Journal, 34, 143. https://doi.org/10.15779/z38m32n986
Casper, S., Ezell, C., Siegmann, C., Kolt, N., Curtis, T. L., Bucknall, B., ... & Hadfield-Menell, D. (2024, June). Black-box access is insufficient for rigorous ai audits. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 2254-2272). https://doi.org/10.1145/3630106.3659037
Cen, S. H., & Alur, R. (2024, October). From transparency to accountability and back: A discussion of access and evidence in AI auditing. In Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (pp. 1-14). https://doi.org/10.1145/3689904.3694711
Cheong, B. (2024). Transparency and accountability in AI systems: Safeguarding wellbeing in the age of algorithmic decision-making. Frontiers in Human Dynamics. https://doi.org/10.3389/fhumd.2024.1421273
Dor, L., & Coglianese, C. (2021). Procurement as AI governance. IEEE Transactions on Technology and Society, 2, 192–199. https://doi.org/10.1109/tts.2021.3111764
Ebers, M. (2024). Truly risk-based regulation of artificial intelligence: How to implement the EU’s AI Act. European Journal of Risk Regulation, 16, 684–703. https://doi.org/10.1017/err.2024.78
Febiandini, V., & Sony, M. (2023). Analysis of public administration challenges in the development of artificial intelligence Industry 4.0. IAIC Transactions on Sustainable Digital Innovation (ITSDI). https://doi.org/10.34306/itsdi.v4i2.586
Gamito, M., & Marsden, C. (2024). Artificial intelligence co-regulation? The role of standards in the EU AI Act. Int. J. Law Inf. Technol., 32. https://doi.org/10.1093/ijlit/eaae011
Golpayegani, D., Pandit, H., & Lewis, D. (2023). To be high-risk, or not to be—semantic specifications and implications of the AI Act’s high-risk AI applications and harmonised standards. Dalam Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3593013.3594050
Golpayegani, D., Pandit, H., & Lewis, D. (2024). AICat: An AI cataloguing approach to support the EU AI Act. arXiv, 164–176. https://doi.org/10.48550/arxiv.2501.04014
Gryz, J., & Rojszczak, M. (2021). Black box algorithms and the rights of individuals: No easy solution to the "explainability" problem. Internet Policy Review, 10. https://doi.org/10.14763/2021.2.1564
Hani, A., Paserangi, H., & Cahyani, A. (2025). The urgency of regulation and governance of artificial intelligence (AI) in Indonesian finance: Comparison and risk-based implementation. Policy Law Notary And Regulatory Issues (POLRI). https://doi.org/10.55047/polri.v4i4.1901
Hassija, V., Chamola, V., Mahapatra, A., Singal, A., Goel, D., Huang, K., Scardapane, S., Spinelli, I., Mahmud, M., & Hussain, A. (2023). Interpreting black-box models: A review on explainable artificial intelligence. Cognitive Computation, 16, 45–74. https://doi.org/10.1007/s12559-023-10179-8
Hickok, M. (2022). Public procurement of artificial intelligence systems: New risks and future proofing. AI & Society, 1–15. https://doi.org/10.1007/s00146-022-01572-2
Hukom, J., Sosial, D., Hukom, R., M., & Info, A. (2025). The effectiveness of artificial intelligence in judicial decision-making in Indonesia. Hakim: Jurnal Ilmu Hukum dan Sosial. https://doi.org/10.51903/hakim.v3i1.2298
Judijanto, L., Solapari, N., & Putra, I. (2024). An analysis of the gap between data protection regulations and privacy rights implementation in Indonesia. The Easta Journal Law and Human Rights. https://doi.org/10.58812/eslhr.v3i01.351
Kamila, Z. (2025). Pengaturan hukum dan prospek penggunaan artificial intelligence dalam era digitalisasi sistem peradilan di Indonesia. Jurnal Riset Multidisiplin Edukasi. https://doi.org/10.71282/jurmie.v2i3.172
Kaminski, M. (2018). The right to explanation, explained. Research Handbook on Information Law and Governance. https://doi.org/10.2139/ssrn.3196985
Kaminski, M., & Malgieri, G. (2019). Algorithmic impact assessments under the GDPR: Producing multi-layered explanations. Cybersecurity. https://doi.org/10.2139/ssrn.3456224
Kirana, K., & Silalahi, W. (2025). Tantangan regulasi kecerdasan buatan (AI) dalam perspektif hukum perlindungan data pribadi di Indonesia. Cerdika: Jurnal Ilmiah Indonesia. https://doi.org/10.59141/cerdika.v5i6.2711
Krafft, T., Hauer, M., & Zweig, K. (2024). Black-box testing and auditing of bias in ADM systems. Minds and Machines, 34. https://doi.org/10.1007/s11023-024-09666-0
Kuziemski, M., & Misuraca, G. (2020). AI governance in the public sector: Three tales from the frontiers of automated decision-making in democratic settings. Telecommunications Policy, 44, 101976. https://doi.org/10.1016/j.telpol.2020.101976
Lee, S. (2022). A black box approach to auditing algorithms. Issues In Information Systems. https://doi.org/10.48009/2_iis_2022_107
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2021). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55, 1–46. https://doi.org/10.1145/3555803
Maghfirli, A., Farina, T., & Ali, N. (2025). Credit scoring P2P lending fintech: Right to explanation in AI-based credit. Eduvest - Journal of Universal Studies. https://doi.org/10.59188/eduvest.v5i11.52362
Malgieri, G., & Comandé, G. (2017). Why a right to legibility of automated decision-making exists in the General Data Protection Regulation. International Data Privacy Law, 7, 243–265. https://doi.org/10.1093/idpl/ipx019
Mavidis, A., & Folinas, D. (2022). From public e-procurement 3.0 to e-procurement 4.0: A critical literature review. Sustainability. https://doi.org/10.3390/su141811252
Mersha, M., Lam, K., Wood, J., AlShami, A., & Kalita, J. (2024). Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction. Neurocomputing, 599, 128111. https://doi.org/10.1016/j.neucom.2024.128111
Metcalf, J., Moss, E., Watkins, E. A., Singh, R., & Elish, M. C. (2021, March). Algorithmic impact assessments and accountability: The co-construction of impacts. In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 735-746). https://doi.org/10.1145/3442188.3445935
Metikoš, L., & Ausloos, J. (2025). The right to an explanation in practice: Insights from case law for the GDPR and the AI Act. Law, Innovation and Technology, 17, 205–240. https://doi.org/10.1080/17579961.2025.2469349
Mökander, J. (2023). Auditing of AI: Legal, ethical and technical approaches. Digital Society, 2. https://doi.org/10.1007/s44206-023-00074-y
Mori?, Z., Daki?, V., Djekic, D., & Regvart, D. (2024). Protection of personal data in the context of e-commerce. J. Cybersecur. Priv., 4, 731–761. https://doi.org/10.3390/jcp4030034
Moss, E., Watkins, E., Singh, R., Elish, M., & Metcalf, J. (2021). Assembling accountability: Algorithmic impact assessment for the public interest. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3877437
Novelli, C., Casolari, F., Rotolo, A., Taddeo, M., & Floridi, L. (2023). Taking AI risks seriously: A new assessment model for the AI Act. AI & SOCIETY, 39, 2493–2497. https://doi.org/10.1007/s00146-023-01723-z
Obinna, A., & Kess-Momoh, J. (2024a). Comparative technical analysis of legal and ethical frameworks in AI-enhanced procurement processes. World Journal of Advanced Research and Reviews. https://doi.org/10.30574/wjarr.2024.22.1.1241
Obinna, A., & Kess-Momoh, J. (2024b). Developing a conceptual technical framework for ethical AI in procurement with emphasis on legal oversight. GSC Advanced Research and Reviews. https://doi.org/10.30574/gscarr.2024.19.1.0149
Obinna, A., & Kess-Momoh, J. (2024c). Systematic technical analysis: Enhancing AI deployment in procurement for optimal transparency and accountability. Global Journal of Engineering and Technology Advances. https://doi.org/10.30574/gjeta.2024.19.1.0067
Papadimitriou, R. (2023). The right to explanation in the processing of personal data with the use of AI systems. International Journal of Law in Changing World. https://doi.org/10.54934/ijlcw.v2i2.53
Rahmawan, A., & Eliana, G. (2022). Deploying AI in taking down Indonesian regulatory problems: A study on early pandemic regulations. Kasetsart Journal of Social Sciences. https://doi.org/10.34044/j.kjss.2022.43.4.16
Saragih, M., Sari, E., & Irfan, I. (2025). Konstruksi transparansi, akuntabilitas, dan partisipasi anggaran dalam mendorong pengelolaan keuangan desa yang baik. Mandiri: Jurnal Akuntansi dan Keuangan. https://doi.org/10.59086/jak.v3i3.644
Scassa, T. (2020). Administrative law and the governance of automated decision-making: A critical look at Canada’s directive on automated decision-making. Social Science Research Network. https://doi.org/10.2139/ssrn.3722192
Schuett, J. (2022). Risk management in the Artificial Intelligence Act. ArXiv, abs/2212.03109. https://doi.org/10.1017/err.2023.1
Selbst, A., & Powles, J. (2017). "Meaningful information" and the right to explanation. [Jurnal tidak teridentifikasi, bersumber dari International Data Privacy Law], 48. https://doi.org/10.1093/idpl/ipx022
Septiawan, R., Anandatia, V., & Gustina, A. (2025). Pemanfaatan artificial intelligence dalam hukum acara pidana: Tinjauan yuridis dan dampak sosial. Perkara: Jurnal Ilmu Hukum dan Politik. https://doi.org/10.51903/perkara.v2i4.2235
Stahl, B., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., Lindqvist, B., Kirichenko, A., Marchal, S., Rodrigues, R., Santiago, N., Warso, Z., & Wright, D. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 1–33. https://doi.org/10.1007/s10462-023-10420-8
Stettinger, G., Weissensteiner, P., & Khastgir, S. (2024). Trustworthiness assurance assessment for high-risk AI-based systems. IEEE Access, 12, 22718–22745. https://doi.org/10.1109/access.2024.3364387
Syam, M., Suratno, S., & Djaddang, S. (2020). Teknik audit perAkun(an) desa: Menuju tata kelola desa yang transparan dan akuntabel. CAPACITAREA: Jurnal Pengabdian kepada Masyarakat. https://doi.org/10.35814/capacitarea.v1i01.1629
Taufiq, M., Kurniawan, M., & Kenyo, A. (2025). The legal protection of personal data in the digital era: A comparative study of Indonesian law and the GDPR. International Journal of Business, Law, and Education. https://doi.org/10.56442/ijble.v6i2.1178
Terzis, P., Veale, M., & Gaumann, N. (2024). Law and the emerging political economy of algorithmic audits. Dalam Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency. https://doi.org/10.1145/3630106.3658970
Wachter, S., Mittelstadt, B., & Floridi, L. (2017). Why a right to explanation of automated decision-making does not exist in the General Data Protection Regulation. International Data Privacy Law, 7, 76–99. https://doi.org/10.2139/ssrn.2903469
Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Cybersecurity. https://doi.org/10.2139/ssrn.3063289
Wadipalapa, R., Katharina, R., Nainggolan, P., Aminah, S., Apriani, T., Ma'rifah, D., & Anisah, A. (2024). An ambitious artificial intelligence policy in a decentralised governance system: Evidence from Indonesia. Journal of Current Southeast Asian Affairs, 43, 65–93. https://doi.org/10.1177/18681034231226393
Wang, X., Wu, Y., Ji, X., & Fu, H. (2024). Algorithmic discrimination: Examining its types and regulatory measures with emphasis on US legal practices. Frontiers in Artificial Intelligence, 7. https://doi.org/10.3389/frai.2024.1320277
Wirtz, B., Weyerer, J., & Kehl, I. (2022). Governance of artificial intelligence: A risk and guideline-based integrative framework. Government Information Quarterly, 39, 101685. https://doi.org/10.1016/j.giq.2022.101685
Wulandari, Y. (2025). Kecerdasan buatan dan perlindungan data: Analisis regulasi yang adil untuk sistem hukum Indonesia. The Juris. https://doi.org/10.56301/juris.v9i1.1518
Yusriadi, Y., Rusnaedi, R., Siregar, N., Megawati, S., & Sakkir, G. (2022). Implementation of artificial intelligence in Indonesia. International Journal of Data and Network Science. https://doi.org/10.5267/j.ijdns.2022.10.005
Zahra, Y. (2025). Ensuring data privacy in the age of artificial intelligence. Journal of Computer Science Application and Engineering (JOSAPEN). https://doi.org/10.70356/josapen.v3i2.66
Zick, T., Kortz, M., Eaves, D., & Doshi-Velez, F. (2024). AI procurement checklists: Revisiting implementation in the age of AI governance. ArXiv, abs/2404.14660. https://doi.org/10.48550/arxiv.2404.14660
Zi?ba, B. (2024). AI Act and public procurement law – an opportunity or threat. Teka Komisji Prawniczej PAN Oddzia? w Lublinie. https://doi.org/10.32084/tkp.9076
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