SavedAdd your journal

Journals / Sayıştay Dergisi / 2025 / Cilt: 36 Sayı: 139

Algorithmic Systems and Democratic Oversight in Public Auditing

Kamu Denetiminde Algoritmik Sistemler ve Demokratik Denetim

Hamza Ateş(İSTANBUL MEDENİYET ÜNİVERSİTESİ)
Pages
681–709
Publication Date
2025-12-24
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Rather than advocating for the wholesale rejection or uncritical embrace of AI, the article argues for a deliberate rethinking of human–machine relations in public administration and auditing, contending that only in this way can the integrity of democratic institutions be safeguarded in the algorithmic age.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This article examines the transformative impact of artificial intelligence (AI) on public auditing, addressing its normative, procedural, and institutional dimensions.
📊
Key Findings & Data:As AI systems are increasingly deployed for functions such as fraud detection, performance monitoring, and predictive compliance, they are reshaping the Weberian foundations of public auditing—traceability, procedural accountability, and human judgment.
💡
Conclusion & Impact:Rather than advocating for the wholesale rejection or uncritical embrace of AI, the article argues for a deliberate rethinking of human–machine relations in public administration and auditing, contending that only in this way can the integrity of democratic institutions be safeguarded in the algorithmic age.
🔬Design:Araştırma Makalesi
📍Scope:Comprehensive Sample
⚙️Variables:Algortimik denetim · kamu hesap verebilirliği · idari etik

Abstract

This article examines the transformative impact of artificial intelligence (AI) on public auditing, addressing its normative, procedural, and institutional dimensions. As AI systems are increasingly deployed for functions such as fraud detection, performance monitoring, and predictive compliance, they are reshaping the Weberian foundations of public auditing—traceability, procedural accountability, and human judgment. Drawing on public administration theory, science and technology studies, and the literature on algorithmic governance, the study investigates how algorithmic opacity, epistemic asymmetries, and machine-based risk logics undermine the democratic legitimacy of the audit function. Through case studies from the Netherlands, Estonia, Brazil, and the United States, it highlights both the opportunities and risks of algorithmic auditing in practice. The article proposes a normative framework built upon four guiding principles—transparency, auditability, contestability, and institutional ethics—to ensure accountable and just integration of AI into public oversight. Rather than advocating for the wholesale rejection or uncritical embrace of AI, the article argues for a deliberate rethinking of human–machine relations in public administration and auditing, contending that only in this way can the integrity of democratic institutions be safeguarded in the algorithmic age.

Özet

Bu makale, yapay zekânın (YZ) kamu denetimi (audit) üzerindeki dönüştürücü etkisini, normatif, prosedürel ve kurumsal boyutlarıyla ele almaktadır. Yolsuzluk tespiti, performans izleme ve öngörücü uyumluluk gibi işlevlerde giderek daha fazla kullanılan YZ sistemleri, kamu denetiminin Weberyen temelleri olan izlenebilirlik, usule uygun hesap verebilirlik ve insan yargısı ilkelerini yeniden şekillendirmektedir. Kamu yönetimi kuramı, bilim ve teknoloji çalışmaları ile algoritmik yönetişim literatüründen yararlanan bu analiz, algoritmik kapalılığın, bilgi eşitsizliklerinin ve makine-temelli risk mantıklarının, denetim işlevinin demokratik meşruiyetini hangi yollarla zayıflattığını araştıtmaktadır. Hollanda, Estonya, Brezilya ve ABD’den vaka örnekleri üzerinden yapılan değerlendirmeler, algoritmik denetimin pratikteki imkânlarını ve risklerini ortaya koymaktadır. Makale, kamu gözetiminde YZ’nin hesap verebilir ve adil biçimde bütünleşmesini sağlamak üzere dört ilkeye dayanan normatif bir çerçeve önermektedir: şeffaflık, denetlenebilirlik, itiraz edilebilirlik ve kurumsal etik. Makalede, YZ’yi toptan reddetmek ya da sınırsızca benimsemek yerine, kamu yönetimi ve denetiminde insan–makine ilişkilerinin yeniden düşünülmesi gerektiği savunulmakta ve demokratik kurumların bütünlüğünü korumanın ancak bu şekilde mümkün olabileceği anlatılmaktadır.

🔖

Cite This Article

Ateş, H. (2025). Algorithmic Systems and Democratic Oversight in Public Auditing. Sayıştay Dergisi, Cilt: 36 Sayı: 139, 681–709. https://doi.org/10.52836/sayistay.1731625

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (39)

  1. Afzal, M. (2025). How public officials perceive algorithmic discretion. Public Administration Review. Advance online publication. https://doi.org/10.1111/puar.13957
  2. Aldemir, C., & Uçma Uysal, T. (2025). Artificial intelligence for financial accountability and governance in the public sector: Strategic opportunities and challenges. Administrative Sciences, 15(2), 58, 1–18. https://doi.org/10.3390/admsci15020058
  3. Alles, M., Kogan, A., & Vasarhelyi, M. A. (2006). Continuous auditing: A new view. Auditing: A Journal of Practice & Theory, 25(2), 207–235.
  4. Ananny, M., & Crawford, K. (2018). Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability. New Media & Society, 20(3), 973–989.
  5. Black, J. (2005). The emergence of risk-based regulation and the new public risk management in the United Kingdom. Public Law, 3(Autumn), 512–549.
  6. Bovens, M. (2007). Analysing and assessing accountability: A conceptual framework. European Law Journal, 13(4), 447–468.
  7. Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1), 1–12.
  8. Damar, M., Köse, H. Ö., Cagle, M. N. and Özen, A. (2024). Mapping the Digital Frontier: Bibliometric and Machine Learning Insights into Public Administration Transformation. TCA Journal/Sayıştay Dergisi, 35(132), 9-41. https://doi. org/10.52836/sayistay.1455036
  9. Danaher, J., Hogan, M. J., Noone, C., Kennedy, R., Behan, A., & Griffith, J. (2017). Algorithmic governance: Developing a research agenda through the power of collective intelligence. Big Data & Society, 4(2), 1–21.
  10. Decker, M. C., Wegner, L., & Leicht-Scholten, C. (2025). Procedural fairness in algorithmic decision-making: The role of public engagement. Ethics and Information Technology, 27(1), 1–15. https://doi.org/10.1007/s10676-024-09811-4
  11. Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv preprint arXiv:1702.08608.
  12. Eslami, M., Kumar, S., & Sandvig, C. (2025). From margins to the table: Charting the potential for public participation in public-sector ADM. In Proceedings of the 2025 ACM Conference on Fairness, Accountability, and Transparency (pp. 1–13). ACM. https:// doi.org/10.1145/3715275.3732173
  13. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin’s Press.
  14. Funda, V. (2025). A systematic review of algorithm auditing processes to assess bias and risks in AI systems. Journal of Informatics and Policy Design, 10(1), 1–18. https:// systems.enpress-publisher.com/index.php/jipd/article/view/11489
  15. GAO (U.S. Government Accountability Office). (2021). Artificial intelligence: An accountability framework for federal agencies and other entities (GAO-21-519SP). https://www. gao.gov/products/gao-21-519sp
  16. GPAI/OECD (2024). Algorithmic transparency in the public sector: A state-of-the-art report of transparency instruments. OECD. https://wp.oecd.ai/app/uploads/2024/12/14- Algorithmic-Transparency-in-the-Public-Sector-A-state-of-the-art-report-oftransparency- instruments.pdf
  17. Kokina, J. (2025). Challenges and opportunities for artificial intelligence in auditing by large public accounting firms. International Journal of Accounting Information Systems, 56, 100671. https://doi.org/10.1016/j.accinf.2025.100671
  18. Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103.
  19. Mahroof, K., Weerakkody, V., Hussain, Z., Irani, Z., & Dwivedi, Y. K. (2025). Navigating power dynamics in the public sector through AI-driven algorithmic decision-making. Government Information Quarterly, 42(3), 101923, 1–12. https://doi.org/10.1016/j. giq.2025.101923
  20. Mökander, J., Axente, M., & Floridi, L. (2021). Confronting ethical challenges in AI. AI & Society, 36(1), 59–67.
  21. Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2021). From what to how: An initial review of publicly available AI ethics tools, methods and research to translate principles into practices. Science and Engineering Ethics, 27(4), 1–22.
  22. Murikah, W. (2024). Bias and ethics of AI systems applied in auditing. Journal of Accounting and Public Policy, 43(4), 100118, 1–12. https://doi.org/10.1016/j. jaccpubpol.2024.100118
  23. Netherlands Court of Audit. (2021). Use of algorithms in central government: Risks and opportunities. https://english.rekenkamer.nl/publications
  24. Nieuwenhuizen, E. (2024). Algorithm registers: A box-ticking exercise or meaningful tool for transparency? Information Polity, 29(4), 397–413. https://doi. org/10.1177/15701255241297107
  25. OECD (2019). Auditing in the digital era: Opportunities, challenges and implications. OECD Publishing.
  26. OECD (2022). The future of auditing in the digital era: Emerging technologies and governance challenges. OECD Publishing.
  27. O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Publishing.
  28. Park, S., & Yoon, J. (2025). AI algorithm transparency, pipelines for trust not prisms: Mitigating general negative attitudes and enhancing trust toward AI. Humanities and Social Sciences Communications, 12, 515. https://doi.org/10.1057/s41599-025-05116-z
  29. Parviainen, J., Koski, A., Eilola, L., Palukka, H., Alanen, P., & Lindholm, C. (2025). Building and eroding the citizen-state relationship in the era of algorithmic decision-making: Towards a new conceptual model of institutional trust. Social Sciences, 14(3), 178, 1-17. https://doi.org/10.3390/socsci14030178
  30. Pasquale, F. (2015). The black box society: The secret algorithms that control money and information. Harvard University Press.
  31. Pérez-Durán, I. (2024). Twenty-five years of accountability research in public administration: Authorship, themes, methods, and future trends. InternationalReview of Administrative Sciences, 90(4), 1161–1188. https://doi.org/10.1177/00208523231211751
  32. Polat, M. (2024). Yapay Zekanın Denetimde Kullanılması ve Etik Sorunlar. Sayıştay Dergisi, 35(134), 395-423. https://doi.org/10.52836/sayistay.1554497
  33. Power, M. (1997). The audit society: Rituals of verification. Oxford University Press.
  34. Strathern, M. (1997). ‘Improving ratings’: Audit in the British university system. European Review, 5(3), 305–321.
  35. Terzis, P., Veale, M., & Gaumann, N. (2024). Law and the emerging political economy of algorithmic audits. In Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency (pp. 1–12). ACM. https://doi. org/10.1145/3630106.3658970
  36. Vasarhelyi, M. A., & Halper, F. B. (1991). The continuous audit of online systems. Auditing: A Journal of Practice & Theory, 10(1), 110-125.
  37. Volodina, T., & Grossi, G. (2024). Digital transformation in public sector auditing: Between hope and fear. Public Management Review, 27(5), 1444-1468. https://doi.org/10.10 80/14719037.2024.2402346
  38. Yavuz, E. (2025). Digitalization in the Fight Against Corruption: Feasibility of a Blockchain- Based System in Türkiye. TCA Journal/Sayıştay Dergisi, 36 (137), 255-287. DOI: https://doi.org/10.52836/sayistay.1675048
  39. Yener, M., Charoenpol, M., Suntharanurak, S. and Köse, H.Ö. (2025). Strategic Cooperation of Supreme Audit Institutions of Thailand and Türkiye for Digital Transformation and Innovation in Public Sector Auditing. TCA Journal/Sayıştay Dergisi, 36 (136), 9-34. https://doi.org/10.52836/sayistay.1633666