AI-Supported Automated Decision-Making in Public Administration: An Evaluation in the Context of Türkiye

Abstract

This study examines the use of artificial intelligence (AI)-based automated decision-making (ADM) systems in public administration in Türkiye. The main objective of the research is to identify the types of ADM systems under which AI applications used in Türkiye’s public administration can be classified, to analyze how these systems align with or diverge from ADM types defined in international literature, and to ultimately develop recommendations for the advancement of ADM systems in Türkiye. The research was conducted using a qualitative method consisting of a semi-systematic literature review and descriptive case analysis. In the first phase, a conceptual framework was developed by reviewing international literature and official documents; in the second phase, selected AI applications in public institutions in Türkiye were evaluated through case analysis. The findings reveal that although AI applications are becoming more widespread in Türkiye’s public administration, ADM systems still face significant challenges regarding transparency, accountability, and legal frameworks. The study highlights the importance of regulatory and strategic improvements in these areas and offers a set of recommendations and policy guidelines for the development of ADM systems in Türkiye.

Keywords

• automated decision making
• artificial intelligence
• Türkiye
• digital transformation

Kamu Yönetiminde Yapay Zekâ Destekli Otomatik Karar Verme: Türkiye Bağlamında Bir Değerlendirme

Abstract

Bu çalışma Türkiye kamu yönetiminde yapay zekâ destekli otomatik karar verme sistemlerinin kullanımını incelemektedir. Çalışmanın temel amacı, Türkiye kamu yönetiminde kullanılan YZ uygulamalarının hangi tür OKV sistemleri kapsamında değerlendirilebileceğini belirlemek ve bu sistemlerin uluslararası literatürde tanımlanan OKV türleriyle hangi açılardan örtüştüğünü ya da ayrıştığını analiz etmek ve sonuç olarak OKV sistemlerinin Türkiye’de geliştirilmesi için öneriler geliştirmektir. Araştırma, yarı-sistematik literatür taraması ve betimleyici vaka analizinden oluşan nitel bir yöntemle yürütülmüştür. İlk aşamada, uluslararası literatür ve resmi belgeler incelenerek kavramsal bir çerçeve oluşturulmuş, ikinci aşamada ise Türkiye’de seçili kamu YZ uygulamaları vaka analizi yoluyla değerlendirilmiştir. Sonuç olarak, Türkiye’de YZ uygulamalarının kamu yönetiminde yaygınlaştığı ancak OKV sistemlerinin şeffaflık, denetim ve yasal çerçeve açısından eksiklikler barındırdığı tespit edilmiştir. Çalışma, bu alanlardaki düzenlemelerin ve stratejik geliştirmelerin önemine dikkat çekerek, Türkiye için öneri ve politika çerçevesi sunmaktadır.

Keywords

• otomatik karar verme
• dijital dönüşüm
• yapay zeka
• türkiye

References

1. Aguirre, S., & Rodriguez, A. (2017, September). Automation of a business process using robotic process automation (RPA): A case study. Applied Computer Sciences in Engineering: 4th Workshop on Engineering Applications, WEA 2017, Cartagena, Colombia, September 27–29, 2017, Proceedings 4 (pp. 65–71). Springer International Publishing.
2. Akman, E., & Çetin, M. (2019, December). Yeni kamu yönetimi anlayışının bir yansıması olarak Dijital Dönüşüm Ofisi. IV. Uluslararası Stratejik ve Sosyal Araştırmalar Sempozyumu (ISASOR 2019), Burdur, Türkiye, 5-6 Aralık, 2019, (pp. 223–231). https://www.isasor.org/ISASOR%20IV%20ABSTRACT%20BOOK.pdf
3. Androutsopoulou, A., Karacapilidis, N., Loukis, E., & Charalabidis, Y. (2019). Transforming the communication between citizens and government through AI-guided chatbots. Government information quarterly, 36(2), 358-367. https://doi.org/10.1016/j.giq.2018.10.001
4. Angwin, J., Larson, J., Mattu, S., & Kirchner, L. (2022). Machine bias. In J. Angwin, J. Larson, S. Mattu, & L. Kirchner (Eds.), Ethics of data and analytics (pp. 254-264). Auerbach Publications. https://doi.org/10.1201/9781003278290
5. Araujo, T., Helberger, N., Kruikemeier, S., & De Vreese, C. H. (2020). In AI we trust? Perceptions about automated decision-making by artificial intelligence. AI & society, 35(3), 611-623. https://doi.org/10.1007/s00146-019-00931-w
6. Asula, M., Makke, J., Freienthal, L., Kuulmets, H. A., & Sirel, R. (2021). Kratt: developing an automatic subject indexing tool for the National Library of Estonia. Cataloging & Classification Quarterly, 59(8), 775-793. https://doi.org/10.1080/01639374.2021.1998283
7. Babšek, M., Ravšelj, D., Umek, L., & Aristovnik, A. (2025). Artificial intelligence adoption in public administration: An overview of top-cited articles and practical applications. AI, 6(3), 44. https://doi.org/10.3390/ai6030044
8. Bayrakcı, E., & Koçman, M. A. (2023). Bilgi Güvenliği ve Elektronik Harp. Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 5(Özel Sayı), 184-206. https://doi.org/10.51124/jneusbf.2023.56

9. Bekker, S. (2020). Fundamental rights in digital welfare states: The case of SyRI in the Netherlands. In Netherlands yearbook of international law 2019: Yearbooks in international law: History, function and future (pp. 289-307). The Hague: TMC Asser Press.
10. Birleşik Krallık Ulusal Denetim Ofisi (2022). Managing tax compliance following the pandemic. https://www.nao.org.uk/wp-content/uploads/2022/12/managing-tax-compliance-following-the-pandemic-report.pdf
11. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at Work, National Bureau of Economic Research working paper 31161.
12. Busch, P. A. (2017, January). The role of contextual factors in the influence of ICT on street-level discretion. Proceedings of the 50th Hawaii International Conference on System Sciences, HICSS, Hawaii, January 4-7, Proceedings (pp. 2963-2972).
13. Bygstad, B. (2017). Generative innovation: a comparison of lightweight and heavyweight IT. Journal of information technology, 32(2), 180-193. https://doi.org/10.1057/jit.2016.15
14. Carter, S., & Nielsen, M. (2017). Using artificial intelligence to augment human intelligence. Distill, 2(12). https://doi.org/10.23915/distill.00009
15. Closs-Davies, S., Burkinshaw, L., & Frecknall-Hughes, J. (2024). Is the HMRC charter fit for purpose? Experiences of tax practitioners and vulnerable citizens. British Tax Review, 2024(2), 304-326.
16. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi (CBDDO) (2021). Ulusal Yapay Zekâ Stratejisi (UYZS) 2021-2025. https://cbddo.gov.tr/SharedFolderServer/Genel/File/TR-UlusalYZStratejisi2021-2025.pdf
17. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi (CBDDO) (2024). Hakkımızda. T.C. Dijital Dönüşüm Ofisi. https://cbddo.gov.tr/hakkimizda/
18. Cumhurbaşkanlığı Dijital Dönüşüm Ofisi (CBDDO) (2025). Kamuda Yapay Zekâ Dönüşümü Projesi, T.C. Dijital Dönüşüm Ofisi https://cbddo.gov.tr/projeler/kayzed/
19. Çekici, O., & Bayrakcı, E. (2024). Yoksullukla Baş Etmede Sürdürülebilir Tarım Politikaları. Necmettin Erbakan Üniversitesi Siyasal Bilgiler Fakültesi Dergisi, 6(1), 87-100. https://doi.org/10.51124/jneusbf.2024.76
20. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı (ÇSB) (2019). 2020-2023 Ulusal Akıllı Şehirler Stratejisi Eylem Planı. https://akillisehirler.gov.tr/wp-content/uploads/EylemPlani.pdf
21. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı (ÇSB) (2024). 2024-2030 Ulusal Akıllı Şehirler Stratejisi Eylem Planı Taslağı. https://www.akillisehirler.gov.tr/wp-content/uploads/2023/12/Taslak-Eylem_Plani.pdf
22. Devlet Hava Meydanları İşletmesi (DHMİ) (2024). Devlet Hava Meydanları İşletmesi 2024 Yılı Faaliyet Raporu. https://www.dhmi.gov.tr/Lists/FaaliyetRaporlari/Attachments/25/54974_ faaliyetraporu2024.pdf
23. Dışişleri Bakanlığı (2025). Hızır yapay zekâ uygulaması. Hızır yapay zekâ uygulaması. http://www.konsolosluk.gov.tr/UseFulLinks/Index
24. Durmuş, N. K., & Erdem, İ. A. (2023). Vergi İdaresi 3.0: Yapay Zekâ Perspektifinden Bir İnceleme. Maliye Dergisi, 184, 225-253.
25. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International journal of information management, 71, 102642.
26. E-Estonia (2020). New e-Estonia factsheet: National AI “Kratt” Strategy. https://e-estonia.com/new-e-estonia-factsheet-national-ai-kratt-strategy/
27. Engel, C., Linhardt, L. & Schubert, M. (2024). Code is law: how COMPAS affects the way the judiciary handles the risk of recidivism. Artificial Intelligence and Law. https://doi.org/10.1007/s10506-024-09389-8 Ertel, W. (2024). Introduction to artificial intelligence. Springer Nature.
28. Etscheid, J. (2018). Automatisierungspotenziale in der Verwaltung. In R. Mohabbat Kar, B. Thapa, & P. Parycek (Eds.), (Un)berechenbar? Algorithmen und Automatisierung in Staat und Gesellschaft. Berlin.
29. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
30. European Union (EU) (2024). The EU Artificial Intelligence Act: The AI Act Explorer. https://artificialintelligenceact.eu/ai-act-explorer/
31. Gelir İdaresi Başkanlığı (GİB) (2025). Dijital Vergi Asistanı GİBİ. https://www.gib.gov.tr/node/170911
32. Gömleksiz, M. (2024). Kamu ve Yükseköğretim Ar-Ge Performansının Özel Sektör Ar-Ge Harcamalarına Etkisi: Seçilmiş OECD Ülkeleri Örneği, Necmettin Erbakan Üniversitesi Sustainable Welfare, 2(2), 96-113.
33. Grimmelikhuijsen, S. (2023). Explaining why the computer says no: Algorithmic transparency affects the perceived trustworthiness of automated decision‐making. Public Administration Review, 83(2), 241-262. https://doi.org/10.1111/puar.13483
34. Hazine ve Maliye Bakanlığı (2025). GİBİ uygulaması. T.C. Hazine ve Maliye Bakanlığı. https://www.gib.gov.tr/mobil-uygulamalar-0
35. HMRC (2019). Tackling tax avoidance, evasion and other forms of non-compliance, HM Report, ISBN 978-1-912809-45-5, PU2245. https://www.gov.uk/government/publications/tackling-tax-avoidance-evasion-and-other-forms-of-non-compliance
36. Houy, C., Hamberg, M., & Fettke, P. (2019, March). Robotic process automation in public administrations. In Digitalisierung von Staat und Verwaltung. Bonn: Gesellschaft für Informatik e.V.. PISSN: 1617-5468. ISBN: 978-3-88579-685-5. Münster. 6.-7 March, 2019, (pp. 62-74). https://dl.gi.de/items/eaf373e1-36c9-4be1-ad8f-59de99deb30a
37. IRRC (2024). Uses of technology accross the Department. https://www.canada.ca/en/immigration-refugees-citizenship/corporate/transparency/digital-transparency-advanced-data-analytics/uses-technology.html ITU (2024). Forest Fires Support Decision System, Projects, https://www.itu.int/net4/wsis/stocktaking/Project/Projects/Details/17055725608247369
38. Juell-Skielse, G., Güner, E. O., & Han, S. (2022, September). Adoption of robotic process automation in the public sector: a survey study in Sweden. In Electronic Government: 21st IFIP WG 8.5 International Conference, EGOV 2022, Linköping, Sweden, September 6–8, 2022, (pp. , 336–352). Proceedings. Springer-Verlag, Berlin, Heidelberg. https://doi.org/10.1007/978-3-031-15086-9_22
39. Kişisel Verileri Koruma Kurumu (KVKK). (2016). 6698 Sayılı Kişisel Verilerin Korunması Kanunu. T.C. Resmî Gazete, 7 Nisan 2016. https://www.mevzuat.gov.tr/mevzuatmetin/1.5.6698.pdf
40. Kratt AI (2025). AI Use Cases, Kratt AI. https://www.kratid.ee/en/ai-use-cases
41. 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(6), 101976. https://doi.org/10.1016/j.telpol.2020.101976
42. Lahey Bölge Mahkemesi (2020). Case number: C-09-550982-HA ZA 18-388. [District Court of The Hague ruling invalidating SyRI].
43. Lindgren, I., Johansson, B., Söderström, F., & Toll, D. (2022, August). Why is it difficult to implement robotic process automation? Empirical cases from Swedish municipalities. In Electronic Government: 21st IFIP WG 8.5 International Conference, EGOV 2022, Linköping, Sweden, September 6–8, 2022, (pp. 353–368). Proceedings. Springer-Verlag, Berlin, Heidelberg. https://doi.org/10.1007/978-3-031-15086-9_23
44. Lindgren, I., Madsen, C. Ø., Hofmann, S., & Melin, U. (2019). Close encounters of the digital kind: A research agenda for the digitalization of public services. Government Information Quarterly, 36(3), 427–436. https://doi.org/10.1016/j.giq.2019.03.002
45. Merriam, S. B. (1998). Qualitative Research and Case Study Applications in Education. Jossey-Bass.
46. Millî Eğitim Bakanlığı (MEB) (2025). Eba ve MEB asistan. T.C. Millî Eğitim Bakanlığı. https://www.meb.gov.tr/eba-asistan-uzaktan-egitimde-cevapsiz-soru-birakmayacak/haber/20829 /tr
47. Mökander, J., Morley, J., Taddeo, M., & Floridi, L. (2021). Ethics-based auditing of automated decision-making systems: Nature, scope, and limitations. Science and Engineering Ethics, 27(4), 44. https://doi.org/10.1007/s11948-021-00319-4
48. Orman Genel Müdürlüğü OGM (2025). Yangın Karar Destek Sistemi, Tanıtım Görseli https://yanginkarardestek.ogm.gov.tr/web/Images/ogm-ekran-en.jpg
49. Oxford Insights (2024). Government AI Readiness Index 2024. https://oxfordinsights.com/ai-readiness/ai-readiness-index/
50. Parycek, P., Schmid, V., & Novak, A. S. (2024). Artificial Intelligence (AI) and automation in administrative procedures: Potentials, limitations, and framework conditions. Journal of the Knowledge Economy, 15(2), 8390-8415. https://doi.org/10.1007/s13132-023-01433-3
51. Rachovitsa, A., & Johann, N. (2022). The human rights implications of the use of AI in the digital welfare state: Lessons learned from the Dutch SyRI case. Human Rights Law Review, 22(2), ngac010. https://doi.org/10.1093/hrlr/ngac010
52. Ramli, M. S. (2024). AI-Assisted Tax Authorities: Leveraging LLM, NLP, and ML for Efficient Tax Audit Reporting. Jurnal BPPK: Badan Pendidikan dan Pelatihan Keuangan, 17(3), 133-144.
53. Ranerup, A., & Henriksen, H. Z. (2019). Value positions viewed through the lens of automated decision-making: The case of social services. Government Information Quarterly, 36(4), 101377. https://doi.org/10.1016/j.giq.2019.05.004
54. Ranerup, A. & Henriksen, H.Z. (2020). Digital discretion: unpacking human and technological agency in automated decision making in Sweden’s social services. Social Science Computer Review, 40(2), 445–461. https://doi.org/10.1177/0894439320980434
55. Rehr, D., & Munteanu, D. (2021). The promise of robotic process automation for the public sector. Center for Business Civic Engagement, George Mason University. https://cbce.gmu.edu/wp-content/uploads/2021/06/The-Promise-of-RPA-For-The-Public-Sector.pdf
56. Reisman, D., Schultz, J., Crawford, K., & Whittaker, M. (2018). Algorithmic impact assessments: a practical Framework for Public Agency. AI Now.
57. Restrepo-Amariles, D. (2020). Algorithmic decision systems: automation and machine learning in the public administration. In W. Barfield (Ed.) The Cambridge Handbook of the Law of Algorithms. (pp. 273-300). Cambridge: Cambridge University Press. https://doi.org/10.1017/9781108680844.015
58. Roehl, U. B. (2022). Understanding automated decision-making in the public sector: a classification of automated, administrative decision-making. In G. Juell-Skielse, I. Lindgren, M. Åkesson (Eds.) Service Automation in the Public Sector: Concepts, Empirical Examples and Challenges (pp. 35-63). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-030-92644-1_3
59. Sağlık Bakanlığı (2025). NeyimVar? uygulaması, Sağlık Bakanlığı, https://neyimvar.gov.tr/giris
60. Salah, M., Abdelfattah, F., & Al Halbusi, H. (2023). Generative Artificial Intelligence (ChatGPT & Bard) in Public Administration Research: A Double-Edged Sword for Street-Level Bureaucracy Studies. International Journal of Public Administration, 1–7. https://doi.org/10.1080/01900692.2023.2274801
61. Snyder, H. (2019). Literature reviews as a research methodology: An overview and guidelines. Journal of Business Research, 104, 333–339.
62. Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368–383. https://doi.org/10.1016/j.giq.2018.09.008
63. Suri, V. K., Elia, M., & van Hillegersberg, J. (2017, February). Software bots-the next frontier for shared services and functional excellence. Global Sourcing of Digital Services: Micro and Macro Perspectives. 11th Global Sourcing Workshop 2017, La Thuile, Italy, February 22-25, 2017, Revised Selected Papers 11 (pp. 81-94). Springer International Publishing
64. Tangi, L., Janssen, M., Benedetti, M., & Noci, G. (2021). Barriers and drivers of Artificial Intelligence adoption in public administrations: Evidence from a systematic literature review. Government Information Quarterly, 38(4), 101593. https://doi.org/10.1016/j.giq.2021.101593
65. Thomas, G. (2011). A Typology for the Case Study in Social Science Following a Review of Definition, Discourse, and Structure. Qualitative Inquiry, 17(6), 511–521. https://doi.org/10.1177/1077800411409884
66. Thomas, G. (2021). How to do your case study: A guide for students and researchers. 3rd ed., Sage.
67. Timmermann, D. (2020). Legal Tech-Anwendungen: Rechtswissenschaftliche Analyse und Entwicklung des Begriffs der algorithmischen Rechtsdienstleistung (3rd ed.). Nomos Verlag.
68. Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TÜBİTAK) (2023). KAMAG 1007 Kamu YZ Ekosistemi Proje Çağrısı. https://tubitak.gov.tr/sites/default/files/1776/1007-cbddo-2023-01_0.pdf
69. Türkiye Bilişim Derneği (TBD) (2024). Kamuda Yapay Zekâ Uygulamaları, Kamubib Çalışma Grubu Raporu, Kasım 2024, https://www.tbd.org.tr/pdf/kamubib-calisma-grubu/kamuda-yapay-zeka-uygulamalari-2024.pdf
70. Ulusal Siber Olaylara Müdahale Merkezi (USOM) (2025). USOM AR-GE Çalışmaları. https://www.usom.gov.tr/arge
71. Väisänen, M. (2022). Implementation of Robotic Process Automation, Case Study Palkeet.
72. Van Kimpren, F., de Bruijn, H., & Arnaboldi, M. (2023). Machine learning algorithms and public decision-making: A conceptual overview. In T. Rana & L. Parker (Eds.) The Routledge Handbook of Public Sector Accounting (pp. 124-138). Routledge.
73. Veale, M., & Brass, I. (2019). Administration by algorithm? Public management meets public sector machine learning. Public Money & Management, 39(5), 377–384. https://doi.org/10.1080/09540962.2019.1583101
74. Wihlborg, E., Larsson, H., & Hedström, K. (2016, January). “The Computer Says No!"--A Case Study on Automated Decision-Making in Public Authorities. 49th Hawaii international conference on system sciences (HICSS), Hawaai, January 5-8, 2016, (pp. 2903-2912). IEEE. https://www.computer.org/csdl/proceedings/hicss/2016/12OmNrMHOd6
75. Willcocks, L., Lacity, M., & Craig, A. (2017). Robotic process automation: strategic transformation lever for global business services? Journal of Information Technology Teaching Cases, 7(1), 17-28. https://doi.org/10.1057/s41266-016-0016-9
76. Wirtz, B. W., Weyerer, J. C., & Geyer, C. (2019). Artificial Intelligence and the public sector—Applications and challenges. International Journal of Public Administration, 42(7), 596–615. https://doi.org/10.1080/01900692.2018.1498103
77. Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505–523. https://doi.org/10.1111/rego.12160
78. Yin, R. K. (2018). Case Study Research and Applications: Design and Methods (6th ed.). Sage Publications. Zouridis, S., van Eck, M., & Bovens, M. (2020). Automated discretion. Information Polity, 25(4), 491–506. https://doi.org/10.3233/IP-190192