Developing AI Regulations in Indonesia: Policy Recommendations Based on Comparative Policy Analysis from the European Union, the United States, and Singapore
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May 10, 2025
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Artificial intelligence (AI) is increasingly becoming the main driver of digital transformation in various sectors such as education, health, and finance. However, this development raises regulatory challenges, particularly regarding privacy protection, algorithm transparency, and AI usage ethics. Indonesia, with a high level of technology adoption but without a specific legal framework, faces the risk of misuse of this technology. This study uses a literature review and a comparative policy analysis approach to examine AI regulations in the European Union, the United States, and Singapore. The results of the analysis show that the European Union emphasizes data protection and strict supervision, the United States promotes flexibility for innovation, while Singapore offers a balance between social responsibility and technological progress. Based on these findings, this article recommends developing an AI regulatory framework in Indonesia by combining the principles of a risk-based approach, transparency, and algorithmic auditing, as well as the establishment of an independent AI regulatory agency. This regulation is expected to encourage innovation that is safe, ethical, and adaptive to technological dynamics.
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Revolusi, P., & Febriandy, R. K. (2025). Developing AI Regulations in Indonesia: Policy Recommendations Based on Comparative Policy Analysis from the European Union, the United States, and Singapore. Jurnal Indonesia : Manajemen Informatika Dan Komunikasi, 6(2), 1035-1049. https://doi.org/10.63447/jimik.v6i2.1380
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References
Bryman, A., & Bell, E. (2019). Social research methods (5th ed.). Oxford University Press.
Buhmann, A., Paßmann, J., & Fieseler, C. (2020). Managing algorithmic accountability: Balancing reputational concerns, engagement strategies, and the potential of rational discourse. Journal of Business Ethics, 163(2), 265–280. https://doi.org/10.1007/s10551-019-04226-4.
Caplan, R. (2018). Algorithmic accountability: A primer. Data & Society, 501.
Celsi, L. R. (2023). The dilemma of rapid AI advancements: Striking a balance between innovation and regulation by pursuing risk-aware value creation. Information, 14(12), 645. https://doi.org/10.3390/INFO14120645.
Commission, E. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. In Brussels (Vol. 0106). https://doi.org/10.3139/9781569902356.023.
Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311–313. https://doi.org/10.1038/538311a.
Erdélyi, O. J., & Goldsmith, J. (2022). Regulating artificial intelligence: Proposal for a global solution. Government Information Quarterly, 39(4), 101748. https://doi.org/10.1016/J.GIQ.2022.101748.
Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A.,
Gupta, J., Hart, C., Jirotka, M., Johnson, H., LaPointe, C., Llorens, A. J., Mackworth, A. K., Maple, C., Pálsson, S. E., Pasquale, F., Winfield, A., & Yeong, Z. K. (2021). Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566–571. https://doi.org/10.1038/s42256-021-00370-7.
Frana, P. L. (2024). Assessing Smart Nation Singapore as an international model for AI responsibility. International Journal on Responsibility, 7(1). https://doi.org/10.62365/2576-0955.1109.
Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI magazine, 38(3), 50-57.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
Horneber, D., & Laumer, S. (2023). Algorithmic accountability. Business and Information Systems Engineering, 65(6), 723–730. https://doi.org/10.1007/S12599-023-00817-8/TABLES/1.
Jannah, L. M., & Si, M. (2022). UU Perlindungan Data Pribadi dan Tantangan Implementasinya. Retrieved August, 16, 2023.
Meiliana, A., Dewi, N. M., & Wijaya, A. (2019). Artificial intelligence in healthcare. Indonesian Biomedical Journal, 11(2), 125–135. https://doi.org/10.18585/inabj.v11i2.844.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data and Society, 3(2). https://doi.org/10.1177/2053951716679679.
Mokander, J., Juneja, P., Watson, D., & Floridi, L. (2024). The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other? Minds and Machines, 32(4), 751–758. https://doi.org/10.1007/s11023-022-09612-y.
Oladele, I., Orelaja, A., & Akinwande, O. T. (2024). Ethical implications and governance of artificial intelligence in business decisions: A deep dive into the ethical challenges and governance issues surrounding the use of artificial intelligence in making critical business decisions. International Journal of Latest Technology in Engineering, Management & Applied Science, XIII(II), 48–56. https://doi.org/10.51583/ijltemas.2024.130207.
Oyewole, A. T., Adeoye, O. B., Addy, W. A., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E. (2024). Automating financial reporting with natural language processing: A review and case analysis. World Journal of Advanced Research and Reviews, 21(3), 575-589. https://doi.org/10.51594/FARJ.V6I4.1035.
Selbst, A. D. (2021). An institutional view of algorithmic impact assessments. Harv. JL & Tech., 35, 117.
Silverman, D. (2020). Qualitative research. SAGE Publications Inc.
Soelistiono, S. (2023). Educational technology innovation: Ai-integrated learning system design in ails-based education. Influence: International Journal of Science Review, 5(2), 470-480. https://doi.org/10.54783/influencejournal.v5i2.175.
Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137–157. https://doi.org/10.1080/14494035.2021.1928377.
Tallberg, J., Erman, E., Furendal, M., Geith, J., Klamberg, M., & Lundgren, M. (2023). The global governance of artificial intelligence: Next steps for empirical and normative research. International Studies Review, 25(3), 40. https://doi.org/10.1093/ISR/VIAD040.
Tallberg, J., Lundgren, M., & Geith, J. (2024). AI regulation in the European Union: Examining non-state actor preferences. Business and Politics, 26(2), 218–239. https://doi.org/10.1017/BAP.2023.36.
Tillu, R., Muthusubramanian, M., & Periyasamy, V. (2023). From data to compliance: The role of AI/ML in optimizing regulatory reporting processes. Journal of Knowledge Learning and Science Technology, 2(3), 381–391. https://doi.org/10.60087/JKLST.VOL2.N3.P391.
Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. van der, Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W. de, … Clopath, C. (2020). AI for social good: Unlocking the opportunity for positive impact. Nature Communications, 11(1), 1–6. https://doi.org/10.1038/s41467-020-15871-z.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again (Vol. 16, Issue 1). Basic Books.
Ulnicane, I. (2022). Artificial intelligence in the European Union: Policy, ethics and regulation. In The Routledge Handbook of European Integrations (pp. 254–269). https://doi.org/10.4324/9780429262081-19.
Voigt, P., & Bussche, A. von dem. (2017). The EU General Data Protection Regulation (GDPR): A practical guide. Springer International Publishing. https://doi.org/10.26512/lstr.v13i2.37425.
Wu, S. (2024). Artificial intelligence-enhanced learning: A new paradigm in the “business data analysis and application” course. Journal of Contemporary Educational Research, 8(2), 164–175. https://doi.org/10.26689/jcer.v8i2.6081.
Yin, R. K. (2015). Qualitative research from start to finish. Guilford Publications.
Zhang, W., Sanmugam, S., Allen, J. G., Wong, W., & Xu, O. (2023). Auditing AI for whom? A community-centric approach to rebuilding public trust in Singapore. SMU Centre for AI & Data Governance Research Paper, 185–200.
Buhmann, A., Paßmann, J., & Fieseler, C. (2020). Managing algorithmic accountability: Balancing reputational concerns, engagement strategies, and the potential of rational discourse. Journal of Business Ethics, 163(2), 265–280. https://doi.org/10.1007/s10551-019-04226-4.
Caplan, R. (2018). Algorithmic accountability: A primer. Data & Society, 501.
Celsi, L. R. (2023). The dilemma of rapid AI advancements: Striking a balance between innovation and regulation by pursuing risk-aware value creation. Information, 14(12), 645. https://doi.org/10.3390/INFO14120645.
Commission, E. (2021). Proposal for a regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (Artificial Intelligence Act) and amending certain Union legislative acts. In Brussels (Vol. 0106). https://doi.org/10.3139/9781569902356.023.
Crawford, K., & Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311–313. https://doi.org/10.1038/538311a.
Erdélyi, O. J., & Goldsmith, J. (2022). Regulating artificial intelligence: Proposal for a global solution. Government Information Quarterly, 39(4), 101748. https://doi.org/10.1016/J.GIQ.2022.101748.
Falco, G., Shneiderman, B., Badger, J., Carrier, R., Dahbura, A., Danks, D., Eling, M., Goodloe, A.,
Gupta, J., Hart, C., Jirotka, M., Johnson, H., LaPointe, C., Llorens, A. J., Mackworth, A. K., Maple, C., Pálsson, S. E., Pasquale, F., Winfield, A., & Yeong, Z. K. (2021). Governing AI safety through independent audits. Nature Machine Intelligence, 3(7), 566–571. https://doi.org/10.1038/s42256-021-00370-7.
Frana, P. L. (2024). Assessing Smart Nation Singapore as an international model for AI responsibility. International Journal on Responsibility, 7(1). https://doi.org/10.62365/2576-0955.1109.
Goodman, B., & Flaxman, S. (2017). European Union regulations on algorithmic decision-making and a “right to explanation”. AI magazine, 38(3), 50-57.
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education promises and implications for teaching and learning. Center for Curriculum Redesign.
Horneber, D., & Laumer, S. (2023). Algorithmic accountability. Business and Information Systems Engineering, 65(6), 723–730. https://doi.org/10.1007/S12599-023-00817-8/TABLES/1.
Jannah, L. M., & Si, M. (2022). UU Perlindungan Data Pribadi dan Tantangan Implementasinya. Retrieved August, 16, 2023.
Meiliana, A., Dewi, N. M., & Wijaya, A. (2019). Artificial intelligence in healthcare. Indonesian Biomedical Journal, 11(2), 125–135. https://doi.org/10.18585/inabj.v11i2.844.
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016). The ethics of algorithms: Mapping the debate. Big Data and Society, 3(2). https://doi.org/10.1177/2053951716679679.
Mokander, J., Juneja, P., Watson, D., & Floridi, L. (2024). The US Algorithmic Accountability Act of 2022 vs. The EU Artificial Intelligence Act: What can they learn from each other? Minds and Machines, 32(4), 751–758. https://doi.org/10.1007/s11023-022-09612-y.
Oladele, I., Orelaja, A., & Akinwande, O. T. (2024). Ethical implications and governance of artificial intelligence in business decisions: A deep dive into the ethical challenges and governance issues surrounding the use of artificial intelligence in making critical business decisions. International Journal of Latest Technology in Engineering, Management & Applied Science, XIII(II), 48–56. https://doi.org/10.51583/ijltemas.2024.130207.
Oyewole, A. T., Adeoye, O. B., Addy, W. A., Okoye, C. C., Ofodile, O. C., & Ugochukwu, C. E. (2024). Automating financial reporting with natural language processing: A review and case analysis. World Journal of Advanced Research and Reviews, 21(3), 575-589. https://doi.org/10.51594/FARJ.V6I4.1035.
Selbst, A. D. (2021). An institutional view of algorithmic impact assessments. Harv. JL & Tech., 35, 117.
Silverman, D. (2020). Qualitative research. SAGE Publications Inc.
Soelistiono, S. (2023). Educational technology innovation: Ai-integrated learning system design in ails-based education. Influence: International Journal of Science Review, 5(2), 470-480. https://doi.org/10.54783/influencejournal.v5i2.175.
Taeihagh, A. (2021). Governance of artificial intelligence. Policy and Society, 40(2), 137–157. https://doi.org/10.1080/14494035.2021.1928377.
Tallberg, J., Erman, E., Furendal, M., Geith, J., Klamberg, M., & Lundgren, M. (2023). The global governance of artificial intelligence: Next steps for empirical and normative research. International Studies Review, 25(3), 40. https://doi.org/10.1093/ISR/VIAD040.
Tallberg, J., Lundgren, M., & Geith, J. (2024). AI regulation in the European Union: Examining non-state actor preferences. Business and Politics, 26(2), 218–239. https://doi.org/10.1017/BAP.2023.36.
Tillu, R., Muthusubramanian, M., & Periyasamy, V. (2023). From data to compliance: The role of AI/ML in optimizing regulatory reporting processes. Journal of Knowledge Learning and Science Technology, 2(3), 381–391. https://doi.org/10.60087/JKLST.VOL2.N3.P391.
Tomašev, N., Cornebise, J., Hutter, F., Mohamed, S., Picciariello, A., Connelly, B., Belgrave, D. C. M., Ezer, D., Haert, F. C. van der, Mugisha, F., Abila, G., Arai, H., Almiraat, H., Proskurnia, J., Snyder, K., Otake-Matsuura, M., Othman, M., Glasmachers, T., Wever, W. de, … Clopath, C. (2020). AI for social good: Unlocking the opportunity for positive impact. Nature Communications, 11(1), 1–6. https://doi.org/10.1038/s41467-020-15871-z.
Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again (Vol. 16, Issue 1). Basic Books.
Ulnicane, I. (2022). Artificial intelligence in the European Union: Policy, ethics and regulation. In The Routledge Handbook of European Integrations (pp. 254–269). https://doi.org/10.4324/9780429262081-19.
Voigt, P., & Bussche, A. von dem. (2017). The EU General Data Protection Regulation (GDPR): A practical guide. Springer International Publishing. https://doi.org/10.26512/lstr.v13i2.37425.
Wu, S. (2024). Artificial intelligence-enhanced learning: A new paradigm in the “business data analysis and application” course. Journal of Contemporary Educational Research, 8(2), 164–175. https://doi.org/10.26689/jcer.v8i2.6081.
Yin, R. K. (2015). Qualitative research from start to finish. Guilford Publications.
Zhang, W., Sanmugam, S., Allen, J. G., Wong, W., & Xu, O. (2023). Auditing AI for whom? A community-centric approach to rebuilding public trust in Singapore. SMU Centre for AI & Data Governance Research Paper, 185–200.