Operationalizing Responsible AI in Health Systems: Delphi Based Governance Metrics for Indonesia
DOI:
https://doi.org/10.61978/data.v3i4.910Keywords:
Responsible AI, Delphi Method, Healthcare Governance, AI Indicators, Indonesia, Clinical Safety, PDP LawAbstract
Artificial intelligence (AI) is rapidly transforming healthcare delivery in Indonesia. However, the responsible governance of AI systems especially in clinical settings remains underdeveloped. This study aims to identify and prioritize measurable governance indicators for AI in Indonesian healthcare through a Delphi based expert consensus process. A three round modified Delphi method was employed, engaging 30 interdisciplinary experts from healthcare, IT, cybersecurity, ethics, law, and patient advocacy. The process began with 40 indicators drawn from global frameworks (WHO, EU AI Act, ISO/IEC 42001, NIST RMF) and national references (UU PDP, SATUSEHAT). Experts rated each indicator on a 1–9 Likert scale across two iterative rounds. Consensus was defined as median ≥7 and IQR ≤1.5 using RAND/UCLA criteria.Out of 40 indicators, 24 achieved consensus. High priority indicators included clinical safety metrics (e.g., AUROC), data privacy compliance (PDP Law documentation), system integration (SATUSEHAT compatibility), and cybersecurity readiness (incident response plans). Transparency related indicators (e.g., training data summaries, model cards) failed to reach consensus, suggesting institutional gaps in AI explainability. The Delphi process underscored the importance of participatory governance, stakeholder trust, and contextual adaptation of international standards. Consensus indicators reflect domains where operational familiarity and regulatory anchors already exist, while non consensus areas highlight the need for capacity building and clearer guidelines. This study delivers a validated, measurable governance framework to guide responsible AI adoption in Indonesian healthcare. It supports policymaking, institutional audits, and procurement strategies aligned with both local regulation and global standards. Future work should pilot these indicators and expand their use in health system assessments and continuous governance improvement.
References
Alanazi, A. (2023). Clinicians’ Views on Using Artificial Intelligence in Healthcare: Opportunities, Challenges, and Beyond. Cureus. https://doi.org/10.7759/cureus.45255 DOI: https://doi.org/10.7759/cureus.45255
Amiot, F., Potier, B., Viard, T., Tellier, É., & Peschanski, N. (2025). Artificial Intelligence (AI) and Emergency Medicine: The Race to the Unknown (Preprint). https://doi.org/10.2196/preprints.70903 DOI: https://doi.org/10.2196/preprints.70903
Arigbabu, A. T., Olaniyi, O. O., Adigwe, C. S., Adebiyi, O. O., & Ajayi, S. A. (2024). Data Governance in AI - Enabled Healthcare Systems: A Case of the Project Nightingale. Asian Journal of Research in Computer Science, 17(5), 85–107. https://doi.org/10.9734/ajrcos/2024/v17i5441 DOI: https://doi.org/10.9734/ajrcos/2024/v17i5441
Arnaout, A., Gill, P., Virani, A., Flatt, A., Prodan-Balla, N., Byres, D., Stowe, M., Saremi, A., Coss, M., Tatto, M., Tuason, M., Malovec, S., & Virani, S. A. (2024). Shaping the Future of Healthcare in British Columbia: Establishing Provincial Clinical Governance for Responsible Deployment of Artificial Intelligence Tools. Healthcare Management Forum, 37(5), 320–328. https://doi.org/10.1177/08404704241264819 DOI: https://doi.org/10.1177/08404704241264819
Bautista, Y. J. P., Theran, C., & Aló, R. (2024). Ethical Considerations of Generative AI: A Survey Exploring the Role of Decision Makers in the Loop. Aaai-Ss, 3(1), 391–398. https://doi.org/10.1609/aaaiss.v3i1.31243 DOI: https://doi.org/10.1609/aaaiss.v3i1.31243
Bhavaraju, S. R. (2023). Artificial Intelligence in Healthcare: Doctor as a Stakeholder. https://doi.org/10.5772/intechopen.111490 DOI: https://doi.org/10.5772/intechopen.111490
Broder, M. S., Gibbs, S. N., & Yermilov, I. (2022). An Adaptation of the RAND/UCLA Modified Delphi Panel Method in the Time of COVID-19. Journal of Healthcare Leadership, Volume 14, 63–70. https://doi.org/10.2147/jhl.s352500 DOI: https://doi.org/10.2147/JHL.S352500
Cath, C. (2018). Governing Artificial Intelligence: Ethical, Legal and Technical Opportunities and Challenges. Philosophical Transactions of the Royal Society a Mathematical Physical and Engineering Sciences, 376(2133), 20180080. https://doi.org/10.1098/rsta.2018.0080 DOI: https://doi.org/10.1098/rsta.2018.0080
Choudhury, A., & Asan, O. (2020). Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review (Preprint). https://doi.org/10.2196/preprints.18599 DOI: https://doi.org/10.2196/preprints.18599
Darom, M., & Plant, E. (2020). The Development of a Performance Measurement System for Indirect Procurement: A Delphi Study. Measuring Business Excellence, 27(4), 579–601. https://doi.org/10.1108/mbe-05-2019-0047 DOI: https://doi.org/10.1108/MBE-05-2019-0047
Drabiak, K. (2022). Leveraging Law and Ethics to Promote Safe and Reliable AI/ML in Healthcare. Frontiers in Nuclear Medicine, 2. https://doi.org/10.3389/fnume.2022.983340 DOI: https://doi.org/10.3389/fnume.2022.983340
Evans, H., & Snead, D. (2024). Understanding the Errors Made by Artificial Intelligence Algorithms in Histopathology in Terms of Patient Impact. NPJ Digital Medicine, 7(1). https://doi.org/10.1038/s41746-024-01093-w DOI: https://doi.org/10.1038/s41746-024-01093-w
Festor, P., Jia, Y., Gordon, A., Faisal, A. A., Habli, I., & Komorowski, M. (2022). Assuring the Safety of AI-based Clinical Decision Support Systems: A Case Study of the AI Clinician for Sepsis Treatment. BMJ Health & Care Informatics, 29(1), e100549. https://doi.org/10.1136/bmjhci-2022-100549 DOI: https://doi.org/10.1136/bmjhci-2022-100549
Foppen, R. J. G., Gioia, V. L., Gupta, S., Johnson, C. L., Giantsidis, J., & Papademetris, M. (2024). Methodology for Safe and Secure AI in Diabetes Management. Journal of Diabetes Science and Technology, 19(3), 620–627. https://doi.org/10.1177/19322968241304434 DOI: https://doi.org/10.1177/19322968241304434
Fors, K. L., & Meissner, F. (2022). Contesting Border Artificial Intelligence: Applying the Guidance-Ethics Approach as a Responsible Design Lens. Data & Policy, 4. https://doi.org/10.1017/dap.2022.28 DOI: https://doi.org/10.1017/dap.2022.28
Friedrich, D. (2023). Are AI Safety and AI Ethics Memetic Rivals? https://doi.org/10.31234/osf.io/3rpwt DOI: https://doi.org/10.31234/osf.io/3rpwt
Gertz, M. A., Abonour, R., Gibbs, S. N., Finkel, M., Landau, H., Lentzsch, S., Lin, G., Mahindra, A., Quock, T. P., Rosenbaum, C. A., Rosenzweig, M., Sidana, S., Tuchman, S. A., Witteles, R., Yermilov, I., & Broder, M. S. (2023). Using a Modified Delphi Panel to Estimate Health Service Utilization for Patients With Advanced and Non-Advanced Systemic Light Chain Amyloidosis. Clinicoeconomics and Outcomes Research, Volume 15, 673–680. https://doi.org/10.2147/ceor.s412079 DOI: https://doi.org/10.2147/CEOR.S412079
Göktaş, P., & Grzybowski, A. (2025). Shaping the Future of Healthcare: Ethical Clinical Challenges and Pathways to Trustworthy AI. Journal of Clinical Medicine, 14(5), 1605. https://doi.org/10.3390/jcm14051605 DOI: https://doi.org/10.3390/jcm14051605
Golpayegani, D., Pandit, H. J., & Lewis, D. (2023). Comparison And Analysis Of 3 Key AI Documents: EU’s Proposed AI Act, Assessment List For Trustworthy AI (ALTAI), And ISO/Iec 42001 AI Management System. 189–200. https://doi.org/10.1007/978-3-031-26438-2_15 DOI: https://doi.org/10.1007/978-3-031-26438-2_15
Hilling, D. E., Ihaddouchen, I., Buijsman, S., Townsend, R., Gommers, D., & Genderen, M. E. v. (2025). The Imperative of Diversity and Equity for the Adoption of Responsible AI in Healthcare. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1577529 DOI: https://doi.org/10.3389/frai.2025.1577529
Humphrey‐Murto, S., Varpio, L., Gonsalves, C., & Wood, T. J. (2016). Using Consensus Group Methods Such as Delphi and Nominal Group in Medical Education Research. Medical Teacher, 39(1), 14–19. https://doi.org/10.1080/0142159x.2017.1245856 DOI: https://doi.org/10.1080/0142159X.2017.1245856
Husnain, A., Rasool, S., Saeed, A., Gill, A. Y., & Hussain, H. K. (2023). AI’S Healing Touch: Examining Machine Learning’s Transformative Effects on Healthcare. Journal of World Science, 2(10), 1681–1695. https://doi.org/10.58344/jws.v2i10.448 DOI: https://doi.org/10.58344/jws.v2i10.448
Hussein, R., Zink, A., Ramadan, B., Howard, F. M., Hightower, M., Shah, S., & Beaulieu‐Jones, B. K. (2024). Advancing Healthcare AI Governance: A Comprehensive Maturity Model Based on Systematic Review. https://doi.org/10.1101/2024.12.30.24319785 DOI: https://doi.org/10.1101/2024.12.30.24319785
Janssen, A. B., Kavisha, S., Johnson, A., Marinic, A., Teede, H., & Shaw, T. (2024). Implementation of Artificial Intelligence Applications in Australian Healthcare Organisations: Environmental Scan Findings. https://doi.org/10.3233/shti231142 DOI: https://doi.org/10.3233/SHTI231142
Kaye, J., Shah, N., Kogetsu, A., Coy, S., Katirai, A., Kuroda, M., Li, Y., Kato, Y., & Yamamoto, B. A. (2024). Moving Beyond Technical Issues to Stakeholder Involvement: Key Areas for Consideration in the Development of Human-Centred and Trusted AI in Healthcare. Asian Bioethics Review, 16(3), 501–511. https://doi.org/10.1007/s41649-024-00300-w DOI: https://doi.org/10.1007/s41649-024-00300-w
Kerr, E. A., Klamerus, M. L., Markovitz, A. A., Sussman, J. B., Bernstein, S. J., Caverly, T., Chou, R., Min, L., Saini, S. D., Lohman, S. E., Skurla, S. E., Goodrich, D. E., Froehlich, W., & Hofer, T. P. (2020). Identifying Recommendations for Stopping or Scaling Back Unnecessary Routine Services in Primary Care. Jama Internal Medicine, 180(11), 1500. https://doi.org/10.1001/jamainternmed.2020.4001 DOI: https://doi.org/10.1001/jamainternmed.2020.4001
Labkoff, S. E., Oladimeji, B. J., Kannry, J., Solomonides, A., Leftwich, R., Koski, E., Joseph, A. L., López-González, M., Fleisher, L. A., Nolen, K., Dutta, S., Levy, D. R., Price, A., Barr, P., Hron, J. D., Lin, B., Srivastava, G., Pastor, N., Luque, U. S., … Quintana, Y. (2024). Toward a Responsible Future: Recommendations for AI-enabled Clinical Decision Support. Journal of the American Medical Informatics Association, 31(11), 2730–2739. https://doi.org/10.1093/jamia/ocae209 DOI: https://doi.org/10.1093/jamia/ocae209
Leenen, J. P. L., Hiemstra, P., Hoeve, M. M. T., Jansen, A., Dijk, J. D. v., Vendel, B. N., Versteeg, G. A. A., Hakvoort, G., & Hettinga, M. (2025). Exploring the Complex Nature of Implementation of Artificial Intelligence in Clinical Practice: An Interview Study With Healthcare Professionals, Researchers and Policy and Governance Experts. Plos Digital Health, 4(5), e0000847. https://doi.org/10.1371/journal.pdig.0000847 DOI: https://doi.org/10.1371/journal.pdig.0000847
Marchenko, A. Y., & Энтин, М. (2022). Artificial Intelligence and Human Rights: What Is the EU’s Approach? Digital Law Journal, 3(3), 43–57. https://doi.org/10.38044/2686-9136-2022-3-3-43-57 DOI: https://doi.org/10.38044/2686-9136-2022-3-3-43-57
Martí‐Bonmatí, L., Koh, D., Riklund, K., Bobowicz, M., Roussakis, Y., Vilanova, J. C., Fütterer, J. J., Rimola, J., Mallol, P., Ribas, G., Miguel, A., Tsiknakis, M., Lekadir, K., & Tsakou, G. (2022). Considerations for Artificial Intelligence Clinical Impact in Oncologic Imaging: An AI4HI Position Paper. Insights Into Imaging, 13(1). https://doi.org/10.1186/s13244-022-01220-9 DOI: https://doi.org/10.1186/s13244-022-01220-9
Mensah, A. K., Apori, S. O., Addai, P., Owusu-Ansah, A., & Owusu-Ansah, D.-G. E. J. (2024). Potentially Harmful Elements in Mining Sites in Ghana: Assessment of Their Carcinogenic and Non-Carcinogenic Health Risks for Children and Adults. Management of Environmental Quality an International Journal, 36(2), 539–561. https://doi.org/10.1108/meq-03-2024-0118 DOI: https://doi.org/10.1108/MEQ-03-2024-0118
Meskó, B., Hetényi, G., & Győrffy, Z. (2018). Will Artificial Intelligence Solve the Human Resource Crisis in Healthcare? BMC Health Services Research, 18(1). https://doi.org/10.1186/s12913-018-3359-4 DOI: https://doi.org/10.1186/s12913-018-3359-4
Mills, B., Conrick, K. M., Anderson, S., Bailes, J. E., Boden, B. P., Conway, D., Ellis, J., Feld, F., Grant, M., Hainline, B., Henry, G., Herring, S. A., Hsu, W. K., Isakov, A., Lindley, T. R., McNamara, L., Mihalik, J. P., Neal, T., Putukian, M., … Courson, R. (2020). Consensus Recommendations on the Prehospital Care of the Injured Athlete With a Suspected Catastrophic Cervical Spine Injury. Journal of Athletic Training, 55(6), 563–572. https://doi.org/10.4085/1062-6050-0434.19 DOI: https://doi.org/10.4085/1062-6050-0434.19
Mudunuri, V. R., Almasri, H., Fan, H.-H., & Chandrasekaran, M. (2025). Safe Deployment of AI and ML Based Software and Algorithms in ADAS Systems. 1. https://doi.org/10.4271/2025-01-8071 DOI: https://doi.org/10.4271/2025-01-8071
Naik, Bn., Mathew, P. J., & Kundra, P. (2024). Scope of Artificial Intelligence in Airway Management. Indian Journal of Anaesthesia, 68(1), 105–110. https://doi.org/10.4103/ija.ija_1228_23 DOI: https://doi.org/10.4103/ija.ija_1228_23
Ndemo, B. (2025). Revolutionizing African Healthcare: A Systematic Review of Artificial Intelligence and Data Governance. https://doi.org/10.21203/rs.3.rs-6572611/v1 DOI: https://doi.org/10.21203/rs.3.rs-6572611/v1
Nong, P., & Ji, M. (2025). Expectations of Healthcare AI and the Role of Trust: Understanding Patient Views on How AI Will Impact Cost, Access, and Patient-Provider Relationships. Journal of the American Medical Informatics Association, 32(5), 795–799. https://doi.org/10.1093/jamia/ocaf031 DOI: https://doi.org/10.1093/jamia/ocaf031
Park, S. H., & Kressel, H. Y. (2018). Connecting Technological Innovation in Artificial Intelligence to Real-World Medical Practice Through Rigorous Clinical Validation: What Peer-Reviewed Medical Journals Could Do. Journal of Korean Medical Science, 33(22). https://doi.org/10.3346/jkms.2018.33.e152 DOI: https://doi.org/10.3346/jkms.2018.33.e152
Potnis, K. C., Ross, J. S., Aneja, S., Gross, C. P., & Richman, I. B. (2022). Artificial Intelligence in Breast Cancer Screening. Jama Internal Medicine, 182(12), 1306. https://doi.org/10.1001/jamainternmed.2022.4969 DOI: https://doi.org/10.1001/jamainternmed.2022.4969
Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022). Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare—A Scoping Review of Reviews. Journal of Personalized Medicine, 12(11), 1914. https://doi.org/10.3390/jpm12111914
Quazi, F. (2024). Ethics &Amp; Responsible AI in Healthcare. https://doi.org/10.21428/e90189c8.a16c4bb9 DOI: https://doi.org/10.2139/ssrn.4942322
Reddy, S., Lebrun, A., Chee, A., & Kalogeropoulos, D. (2024). Discussing the Role of Explainable AI and Evaluation Frameworks for Safe and Effective Integration of Large Language Models in Healthcare. Telehealth and Medicine Today, 9(2). https://doi.org/10.30953/thmt.v9.485 DOI: https://doi.org/10.30953/thmt.v9.485
Reddy, S., Rogers, W., Mäkinen, V., Coiera, E., Brown, P., Wenzel, M., Weicken, E., Ansari, S., Mathur, P., Casey, A., & Kelly, B. (2021). Evaluation Framework to Guide Implementation of AI Systems Into Healthcare Settings. BMJ Health & Care Informatics, 28(1), e100444. https://doi.org/10.1136/bmjhci-2021-100444 DOI: https://doi.org/10.1136/bmjhci-2021-100444
Salwei, M. E., & Carayon, P. (2022). A Sociotechnical Systems Framework for the Application of Artificial Intelligence in Health Care Delivery. Journal of Cognitive Engineering and Decision Making, 16(4), 194–206. https://doi.org/10.1177/15553434221097357 DOI: https://doi.org/10.1177/15553434221097357
Schults, J., Charles, K., Long, D., Erikson, S., Brown, G., Waak, M., Tume, L. N., Hall, L., & Ullman, A. (2022). Appropriate Use Criteria for Endotracheal Suction Interventions in Mechanically Ventilated Children: The RAND/UCLA Development Process. Australian Critical Care, 35(6), 661–667. https://doi.org/10.1016/j.aucc.2021.10.006 DOI: https://doi.org/10.1016/j.aucc.2021.10.006
Solberg, E., Kaarstad, M., Eitrheim, M. H. R., Bisio, R., Reegård, K., & Bloch, M. (2022). A Conceptual Model of Trust, Perceived Risk, and Reliance on AI Decision Aids. Group & Organization Management, 47(2), 187–222. https://doi.org/10.1177/10596011221081238 DOI: https://doi.org/10.1177/10596011221081238
Sriharan, A., Kuhlmann, E., Correia, T., Tahzib, F., Czabanowska, K., Ungureanu, M., & Kumar, B. N. (2025). Artificial Intelligence in Healthcare: Balancing Technological Innovation With Health and Care Workforce Priorities. The International Journal of Health Planning and Management. https://doi.org/10.1002/hpm.3927 DOI: https://doi.org/10.1002/hpm.3927
Sulaiman, A. H., Amin, M. M., Ang, J. K., Ho, R., Jaafar, N. R. N., Guan, N. C., Nurhidayat, A. W., Paholpak, P., Pariwatcharakul, P., Sanguanvichaikul, T., Ung, K. E. K., Wardani, N. D., & Yeo, B. T. T. (2025). Expert Guidelines on the Use of Cariprazine in Bipolar I Disorder: Consensus From Southeast Asia. Healthcare, 13(11), 1304. https://doi.org/10.3390/healthcare13111304 DOI: https://doi.org/10.3390/healthcare13111304
Trajkovski, G. (2024). Bridging the Public Administration‐AI Divide: A Skills Perspective. Public Administration and Development, 44(5), 412–426. https://doi.org/10.1002/pad.2061 DOI: https://doi.org/10.1002/pad.2061
Tsopra, R., Fernández, X. M., Luchinat, C., Alberghina, L., Lehrach, H., Vanoni, M., Dreher, F., Sezerman, U., Cuggia, M., Tayrac, M. d., Miklaševičs, E., Itu, L., Geantă, M., Ogilvie, L. A., Godey, F., Boldișor, C., Campillo‐Gimenez, B., Cioroboiu, C., Ciusdel, C., … Burgun, A. (2021). A Framework for Validating AI in Precision Medicine: Considerations From the European ITFoC Consortium. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01634-3 DOI: https://doi.org/10.1186/s12911-021-01634-3
Unogwu, O. J., Ike, M., & Joktan, O. O. (2023). Employing Artificial Intelligence Methods in Drug Development: A New Era in Medicine. Mjaih, 2023, 52–56. https://doi.org/10.58496/mjaih/2023/010 DOI: https://doi.org/10.58496/MJAIH/2023/010
Vloet, L., Hesselink, G., Berben, S. A. A., Hoogeveen, M., Rood, P., & Ebben, R. H. (2021). The Updated National Research Agenda 2021–2026 for Prehospital Emergency Medical Services in the Netherlands: A Delphi Study. Scandinavian Journal of Trauma Resuscitation and Emergency Medicine, 29(1). https://doi.org/10.1186/s13049-021-00971-6 DOI: https://doi.org/10.1186/s13049-021-00971-6
Woo, K., Ulloa, J. G., Allon, M., Carsten, C. G., Chemla, E. S., Henry, M. L., Huber, T. S., Lawson, J. H., Lok, C. E., Peden, E. K., Scher, L. A., Sidawy, A. N., Maggard‐Gibbons, M., & Cull, D. L. (2017). Establishing Patient-Specific Criteria for Selecting the Optimal Upper Extremity Vascular Access Procedure. Journal of Vascular Surgery, 65(4), 1089-1103.e1. https://doi.org/10.1016/j.jvs.2016.10.099 DOI: https://doi.org/10.1016/j.jvs.2016.10.099
Yuen, K. C., Moraitis, A., & Nguyen, D. (2017). Evaluation of Evidence of Adrenal Insufficiency in Trials of Normocortisolemic Patients Treated With Mifepristone. Journal of the Endocrine Society, 1(4), 237–246. https://doi.org/10.1210/js.2016-1097 DOI: https://doi.org/10.1210/js.2016-1097
Zaidi, S., Shaikh, A., & Surani, S. (2024). The Pulse of AI: Implementation of Artificial Intelligence in Healthcare and Its Potential Hazards. The Open Respiratory Medicine Journal, 18(1). https://doi.org/10.2174/0118743064289936240115105057 DOI: https://doi.org/10.2174/0118743064289936240115105057
Zanardo, G., Coimbra, J. R., & Silva, D. F. d. (2025). Improving Diagnosis of Cardiovascular Diseases by Artificial Intelligence in Electrocardiography. Centro De Pesquisas Avançadas Em Qualidade De Vida, 17(2), 11. https://doi.org/10.36692/v17n2-52r DOI: https://doi.org/10.36692/V17N2-52R
Zhao, H., Coston, A., Adel, T., & Gordon, G. J. (2019). Conditional Learning of Fair Representations. https://doi.org/10.48550/arxiv.1910.07162
Zinchenko, V. V., Arzamasov, K. M., Chetverikov, S. F., Maltsev, A. V., Novik, V., Akhmad, E. S., Sharova, D. E., Andreychenko, A. E., Vladzymyrskyy, A. V., & Morozov, S. P. (2022). Methodology for Conducting Post-Marketing Surveillance of Software as a Medical Device Based on Artificial Intelligence Technologies. Sovremennye Tehnologii v Medicine, 14(5), 15. https://doi.org/10.17691/stm2022.14.5.02 DOI: https://doi.org/10.17691/stm2022.14.5.02
Zsidai, B., Hilkert, A., Kaarre, J., Narup, E., Senorski, E. H., Grassi, A., Ley, C., Longo, U. G., Herbst, E., Hirschmann, M. T., Kopf, S., Seil, R., Tischer, T., Samuelsson, K., & Feldt, R. (2023). A Practical Guide to the Implementation of AI in Orthopaedic Research – Part 1: Opportunities in Clinical Application and Overcoming Existing Challenges. Journal of Experimental Orthopaedics, 10(1). https://doi.org/10.1186/s40634-023-00683-z DOI: https://doi.org/10.1186/s40634-023-00683-z
Сулейманова, С. Р. (2024). Comparative Legal Analysis of the Role of Artificial Intelligence in Human Rights Protection: Prospects for Europe and the Middle East. PJC, 16.3, 907–922. https://doi.org/10.62271/pjc.16.3.907.922 DOI: https://doi.org/10.62271/pjc.16.3.907.922
