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Dijital Sosyal Koruma İçin Çok Katmanlı Bir Yapay Zeka Modeli: Türkiye Örneği

Yıl 2025, Sayı: Özel Sayı, 187 - 209, 10.06.2025

Mehmet Erçorumlu

https://doi.org/10.21441/sosyalguvence.1682319

Öz

Bu çalışma, yapay zekâ (YZ), büyük veri ve dijital teknolojilerin Türkiye'deki sosyal güvenlik ve sosyal hizmet sistemlerine entegrasyonunu incelemeyi amaçlamaktadır. Sosyal koruma alanının iki temel bileşeni olan sosyal güvenlik ve sosyal hizmetler, farklı kurumsal yapılara sahip olsalar da, günümüzde dijital dönüşüm gereksinimleri ve veri temelli yönetişim anlayışı çerçevesinde giderek daha fazla bütünleşmektedir. Çalışma, YZ’nin yalnızca teknik bir araç olarak değil, aynı zamanda kamu hizmetlerinin yeniden yapılandırılmasında stratejik bir unsur olarak ele alınması gerektiğini ortaya koymaktadır. Bu kapsamda, literatürdeki yönetişim yaklaşımları, sosyal hizmet müdahale döngüsü ve kamu yönetiminde dijital kapasite gelişimi temel alınarak tematik ve analitik bir model önerisi geliştirilmiştir. Model; iletişim, teşhis, programlama, uygulama ve değerlendirme aşamalarından oluşan sosyal hizmet süreçlerine YZ teknolojilerinin nasıl entegre edilebileceğini sistematik bir şekilde ortaya koymakta; her bir sürecin YZ ile desteklenebilirliğini ve uygulama olanaklarını değerlendirmektedir. Ayrıca, kurumsal dijitalleşmenin önündeki başlıca engellerden biri olan etik çerçeve eksikliği, veri bütünlüğü sorunları ve sınırlı dijital okuryazarlık düzeyine dikkat çekilmektedir. Çalışmanın bulguları, Türkiye’de sosyal koruma alanında bütüncül, katılımcı ve insan merkezli bir dijital dönüşüm için stratejik bir yönetişim yaklaşımına ihtiyaç duyulduğunu göstermektedir. Bu doğrultuda çalışma, kamu kurumlarının yapay zekâ tabanlı hizmet sunumuna geçişinde yol gösterici olabilecek uygulamalı bir model ve politika önerileri sunarak literatüre özgün bir katkı sağlamaktadır.

Anahtar Kelimeler

Yapay Zekâ Sosyal Koruma Dijital Dönüşüm Veri Yönetişimi Etik Yönetişim

Kaynakça

  • Aibar Bernard, J. (2020). Big Data and data analysis applied by the General Treasury of Social Security as a means of combating fraud in Social Security. Trabajo y derecho: new journal of current affairs and labour relations, 2020, extra no. 11, p. 2.
  • Almeida, F. (2017). Concepts and Fundaments of Data Warehousing and OLAP. INESC TEC and University of Porto, 1, 1–40.
  • Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial Intelligence in Organizations: Current State and Future Opportunities (December 3, 2020). MIS Quarterly Executive, Vol. 19(4), Article 4. Available at SSRN: https://ssrn.com/abstract=3741983 or http://dx.doi.org/10.2139/ssrn.3741983
  • Berryhill, J., Heang, K. K., Clogher, R., & McBride, K. (2019). Hello, World: Artificial intelligence and its use in the public sector. Retrieved October 23, 2024, from https://www.oecd-ilibrary.org/content/paper/726fd39d-en
  • Boucher, P. (2020). Artificial intelligence: How does it work, why does it matter, and what can we do about it? EPRS: European Parliamentary Research Service, Belgium. Retrieved October 30, 2024, from https://coilink.org/20.500.12592/00dvnz
  • Burgess, A. (2017). The Executive Guide to Artificial Intelligence: How to identify and implement applications for AI in your organization. Springer.
  • Cetina, C. (2020). Three Questions on Using Data to Fight Corruption (Policy Brief No. 9). Caracas: Development Bank of Latin America.
  • Chan, C., & Holosko, M. J. (2018). Technology for Social Work Interventions. In E. M. Mullen (Ed.), Oxford Bibliographies in Social Work. Oxford University Press. European Commission. (2019). Ethics Guidelines for Trustworthy AI (p. 14).
  • Fischer, S. C., & Wenger, A. (2021). Artificial intelligence, forward‐looking governance and the future of security. Swiss Political Science Review, 27(1), 170–179.
  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
  • Goldkind, L. (2021). Social work and artificial intelligence: Into the matrix (Social Service Faculty Publications No. 19). Retrieved October 23, 2024, from https://research.library.fordham.edu/gss_facultypubs/19
  • Grinyaev, S. N., Medvedev, D. A., Pravikov, D. I., Samarin, I. V., & Silantyev, A. U. (2021). The role of artificial intelligence technologies in long-term socio-economic development and integrated security. Periodicals of Engineering and Natural Sciences, 9(3), 153–168.
  • Hassan, B. (2022). Artificial intelligence in social security: Opportunities and challenges. Journal of Social Policy Research, 20(3), 407–418.
  • Howe, D. (2009). A Brief Introduction to Social Work Theory. London: Red Globe Press. https://doi.org/10.1007/978-0-230-36523-0
  • Kitchin, R. (2014). The Data Revolution: Big data, open data, data infrastructures and their consequences. Sage.
  • OECD. (2021). Rethinking public institutions in the digital age. Paris: OECD.
  • Perry, J. L. (2020). Managing organizations to sustain passion for public service. Cambridge University Press.
  • Raimundo, R., & Rosário, A. (2021). The impact of artificial intelligence on data system security: A literature review. Sensors, 21(21), 7029.
  • Resmî Gazete. (2014, February 15). 28914 sayılı Bilgi Teknolojileri ve İletişim Kurumu İdari Yaptırımlar Yönetmeliği. Retrieved October 20, 2024, from https://www.resmigazete.gov.tr/eskiler/2014/02/20140215-7.htm
  • Saki, Ş., & Köroğlu, M. A. (2024). Yapay zeka ve dijital teknolojilerin sosyal hizmet uygulamalarında kullanımı üzerine bir araştırma. Journal of Social Humanities and Administrative Sciences (JOSHAS).
  • Saveliev, A., & Zhurenkov, D. (2021). Artificial intelligence and social responsibility: The case of the AI strategies in the United States, Russia, and China. Kybernetes, 50(3), 656–675.
  • 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.
  • Swain, T. (2024). AI-driven social protection: Enhancing access and delivery. CSM Technologies Blog.
  • Türkiye Bilişim Derneği. (2024). Kamuda yapay zeka uygulamaları 2024 (Çalışma Grubu Raporu). Türkiye Bilişim Derneği.
  • Ubaldi, B., Le Fevre, E. M., Petrucci, E., Marchionni, P., Biancalana, C., Hiltunen, N., … & Yang, C. (2019). State of the art in the use of emerging technologies in the public sector. OECD Working Papers on Public Governance, (31), 1–74.
  • Wang, D., Lin, T., & Xu, H. (2022). The theoretical topology and implementation of enterprise social security in the digital age based on big data and artificial intelligence. Journal of Sensors, 2022, Article 7814886. https://doi.org/10.1155/2022/7814886
  • Weill, P., & Ross, J. W. (2004). IT governance: How top performers manage IT decision rights for superior results. Harvard Business Press.
  • West, D., & Heath, D. (2011). Theoretical pathways to the future: Globalization, ICT and social work theory and practice. Journal of Social Work, 11, 209–221. https://doi.org/10.1177/1468017310386835
  • 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.

A Multi-Layered Artificial Intelligence Model For Digital Social Protection: The Case Of Turkey

Yıl 2025, Sayı: Özel Sayı, 187 - 209, 10.06.2025

Mehmet Erçorumlu

https://doi.org/10.21441/sosyalguvence.1682319

Öz

This study explores the integration of artificial intelligence (AI), big data, and digital transformation into Turkey's social security and social service systems. Although traditionally managed under distinct institutional frameworks, these two pillars of social protection are increasingly converging in terms of policy objectives and technological challenges. The research underscores that the transformation of social protection systems requires more than the procurement of digital tools; it necessitates a profound restructuring of governance culture, institutional design, and human capital strategies. Based on international best practices and national case analysis, the study proposes a thematic and analytical model for integrating AI into the social welfare cycle, including communication, assessment, planning, implementation, and evaluation. The findings highlight the fragmented implementation of AI in the social services sector and the lack of a unified legal and ethical framework as key barriers to scaling up AI-based systems. Furthermore, the study emphasizes the strategic role of interoperable data infrastructures and outlines the potential of centralized data warehouses to support intelligent public administration. A phased integration model is introduced, focusing on regulatory harmonization, process automation, predictive analytics, and ethical oversight. Policy recommendations include the creation of cross-sectoral AI task forces, alignment with European regulatory standards, multi-level training programs, and the design of inclusive, transparent, and accountable AI systems. By bridging theory and practice, this study offers a comprehensive framework that contributes to the growing discourse on AI in public governance and outlines future directions for empirical research.

Anahtar Kelimeler

Artificial Intelligence Social Protection Digital Transformation Data Governance Ethical Governance

Kaynakça

  • Aibar Bernard, J. (2020). Big Data and data analysis applied by the General Treasury of Social Security as a means of combating fraud in Social Security. Trabajo y derecho: new journal of current affairs and labour relations, 2020, extra no. 11, p. 2.
  • Almeida, F. (2017). Concepts and Fundaments of Data Warehousing and OLAP. INESC TEC and University of Porto, 1, 1–40.
  • Benbya, H., Davenport, T. H., & Pachidi, S. (2020). Artificial Intelligence in Organizations: Current State and Future Opportunities (December 3, 2020). MIS Quarterly Executive, Vol. 19(4), Article 4. Available at SSRN: https://ssrn.com/abstract=3741983 or http://dx.doi.org/10.2139/ssrn.3741983
  • Berryhill, J., Heang, K. K., Clogher, R., & McBride, K. (2019). Hello, World: Artificial intelligence and its use in the public sector. Retrieved October 23, 2024, from https://www.oecd-ilibrary.org/content/paper/726fd39d-en
  • Boucher, P. (2020). Artificial intelligence: How does it work, why does it matter, and what can we do about it? EPRS: European Parliamentary Research Service, Belgium. Retrieved October 30, 2024, from https://coilink.org/20.500.12592/00dvnz
  • Burgess, A. (2017). The Executive Guide to Artificial Intelligence: How to identify and implement applications for AI in your organization. Springer.
  • Cetina, C. (2020). Three Questions on Using Data to Fight Corruption (Policy Brief No. 9). Caracas: Development Bank of Latin America.
  • Chan, C., & Holosko, M. J. (2018). Technology for Social Work Interventions. In E. M. Mullen (Ed.), Oxford Bibliographies in Social Work. Oxford University Press. European Commission. (2019). Ethics Guidelines for Trustworthy AI (p. 14).
  • Fischer, S. C., & Wenger, A. (2021). Artificial intelligence, forward‐looking governance and the future of security. Swiss Political Science Review, 27(1), 170–179.
  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting and Social Change, 114, 254–280.
  • Goldkind, L. (2021). Social work and artificial intelligence: Into the matrix (Social Service Faculty Publications No. 19). Retrieved October 23, 2024, from https://research.library.fordham.edu/gss_facultypubs/19
  • Grinyaev, S. N., Medvedev, D. A., Pravikov, D. I., Samarin, I. V., & Silantyev, A. U. (2021). The role of artificial intelligence technologies in long-term socio-economic development and integrated security. Periodicals of Engineering and Natural Sciences, 9(3), 153–168.
  • Hassan, B. (2022). Artificial intelligence in social security: Opportunities and challenges. Journal of Social Policy Research, 20(3), 407–418.
  • Howe, D. (2009). A Brief Introduction to Social Work Theory. London: Red Globe Press. https://doi.org/10.1007/978-0-230-36523-0
  • Kitchin, R. (2014). The Data Revolution: Big data, open data, data infrastructures and their consequences. Sage.
  • OECD. (2021). Rethinking public institutions in the digital age. Paris: OECD.
  • Perry, J. L. (2020). Managing organizations to sustain passion for public service. Cambridge University Press.
  • Raimundo, R., & Rosário, A. (2021). The impact of artificial intelligence on data system security: A literature review. Sensors, 21(21), 7029.
  • Resmî Gazete. (2014, February 15). 28914 sayılı Bilgi Teknolojileri ve İletişim Kurumu İdari Yaptırımlar Yönetmeliği. Retrieved October 20, 2024, from https://www.resmigazete.gov.tr/eskiler/2014/02/20140215-7.htm
  • Saki, Ş., & Köroğlu, M. A. (2024). Yapay zeka ve dijital teknolojilerin sosyal hizmet uygulamalarında kullanımı üzerine bir araştırma. Journal of Social Humanities and Administrative Sciences (JOSHAS).
  • Saveliev, A., & Zhurenkov, D. (2021). Artificial intelligence and social responsibility: The case of the AI strategies in the United States, Russia, and China. Kybernetes, 50(3), 656–675.
  • 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.
  • Swain, T. (2024). AI-driven social protection: Enhancing access and delivery. CSM Technologies Blog.
  • Türkiye Bilişim Derneği. (2024). Kamuda yapay zeka uygulamaları 2024 (Çalışma Grubu Raporu). Türkiye Bilişim Derneği.
  • Ubaldi, B., Le Fevre, E. M., Petrucci, E., Marchionni, P., Biancalana, C., Hiltunen, N., … & Yang, C. (2019). State of the art in the use of emerging technologies in the public sector. OECD Working Papers on Public Governance, (31), 1–74.
  • Wang, D., Lin, T., & Xu, H. (2022). The theoretical topology and implementation of enterprise social security in the digital age based on big data and artificial intelligence. Journal of Sensors, 2022, Article 7814886. https://doi.org/10.1155/2022/7814886
  • Weill, P., & Ross, J. W. (2004). IT governance: How top performers manage IT decision rights for superior results. Harvard Business Press.
  • West, D., & Heath, D. (2011). Theoretical pathways to the future: Globalization, ICT and social work theory and practice. Journal of Social Work, 11, 209–221. https://doi.org/10.1177/1468017310386835
  • 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.
Toplam 29 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Sosyal Güvenlik
Bölüm Makaleler
Yazarlar

Mehmet Erçorumlu 0000-0003-1609-3742

Erken Görünüm Tarihi 10 Haziran 2025
Yayımlanma Tarihi 10 Haziran 2025
Gönderilme Tarihi 23 Nisan 2025
Kabul Tarihi 17 Mayıs 2025
Yayımlandığı Sayı Yıl 2025 Sayı: Özel Sayı

Kaynak Göster

APA Erçorumlu, M. (2025). Dijital Sosyal Koruma İçin Çok Katmanlı Bir Yapay Zeka Modeli: Türkiye Örneği. Sosyal Güvence(Özel Sayı), 187-209. https://doi.org/10.21441/sosyalguvence.1682319
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