THE PRESENTATION OF PUBLIC BUDGET DATA WITH ARTIFICIAL INTELLIGENCE: TECHNOLOGICAL TRANSFORMATION AND CHALLENGES

Öz

The use of artificial intelligence (AI) technologies in the public sector and finance has become a significant research topic in recent years. This study aims to evaluate the accuracy of AI models in providing and analyzing public budget data. In this research, the accuracy levels of six different AI models (ChatSonic, Claude, ChatGPT, Perplexity, Gemini, Copilot) in relation to the public budget data for the period 2009-2023 have been examined. Gemini and Copilot were excluded from the evaluation due to their inability to provide complete or reliable data. The ChatSonic and Claude models provided data closer to the actual values compared to ChatGPT and Perplexity. However, even the data provided by these models exhibited unacceptable discrepancies from the actual data, particularly in the revenue and expenditure categories. Furthermore, the models presented the expenditure data more accurately, while the error rate for revenue data was higher. Additionally, it was observed that accuracy was higher for older data, while error rates increased as the data approached more recent years. It was also found that the AI models did not account for fractions, which could result in significant discrepancies amounting to millions of Liras. The study reveals that while AI models are limited in meeting the high accuracy requirements for sensitive data such as public budgets. However, ChatSonic and Claude can provide more accurate data, holding potential as decision-making tools in future budgeting processes. These findings offer important implications for the future role of AI technologies in public finance.

Anahtar Kelimeler

• Artificial Intelligence
• Public Budgeting
• Data Reliability
• Decision-Making Systems
• AI Models Evaluation

YAPAY ZEKÂ İLE KAMU BÜTÇESİ VERİLERİNİN SUNUMU: TEKNOLOJİK DÖNÜŞÜM VE SORUNLAR

Öz

Yapay zekâ (YZ) teknolojilerinin kamu sektörü ve maliye alanındaki kullanımı son yıllarda önemli bir araştırma konusu olmuştur. Bu çalışma, YZ modellerinin kamu bütçesi verilerini sağlama ve analiz etme süreçlerindeki doğruluklarını değerlendirmeyi amaçlamaktadır. Çalışmada, 2009-2023 dönemi için altı farklı YZ modelinin (ChatSonic, Claude, ChatGPT, Perplexity, Gemini, Copilot) kamu bütçesi verilerinin sunumundaki doğruluk seviyeleri incelenmiştir. Gemini ve Copilot eksik yahut hiç veri sağlayamadığı için değerlendirmeye alınmamıştır. ChatSonic ve Claude, ChatGPT ve Perplexity’ye göre gerçeğe daha yakın veriler sunmuştur. Ancak bu modellerin sunduğu veriler dahi, gerçek verilerden, özellikle gelir ve gider kalemlerinde, kabul edilemeyecek seviyede farklılık göstermiştir. Ayrıca modellerde harcama verilerinin daha doğru sunulduğu, gelir verilerindeki hata oranlarının ise daha yüksek olduğu görülmüştür. Ek olarak, eski yıllara ait verilerde doğruluk oranı daha yüksekken, güncel verilere yaklaşıldıkça hata oranının arttığı gözlemlenmiştir. YZ modellerinin, milyonlarca lira anlamına gelen küsuratları dikkate almadığı da anlaşılmıştır. Çalışma, YZ modellerinin kamu bütçesi gibi hassas verilerde yüksek doğruluk gereksinimlerini karşılama noktasında sınırlı kaldığını, ancak gelecekte, daha doğru veri sunabilen ChatSonic ve Claude modellerinin, bütçeleme süreçlerinde karar destek aracı olarak potansiyel taşıdığını göstermektedir. Bu bulgular, YZ teknolojilerinin kamu maliyesindeki geleceği için önemli çıkarımlar sunmaktadır.

Anahtar Kelimeler

• Yapay Zekâ
• Kamu Bütçesi
• Veri Güvenilirliği
• Karar Alma Sistemleri
• YZ Modellerinin Değerlendirilmesi

Kaynakça

1. Acemoglu, D., and Restrepo, P. (2018a). Artificial Intelligence, Automation and Work. NBER Working Papers 24196. doi:10.3386/w24196
2. Acemoglu, D., and Restrepo, P. (2018b). Modeling Automation. AEA Papers and Proceedings, 48–53. doi:10.1257/pandp.20181020
3. Acemoglu, D., and Restrepo, P. (2018c). The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. American Economic Review, 108(6), 1488–1542. doi:10.1257/aer.20160696
4. Acemoglu, D., and Restrepo, P. (2020). Robots and Jobs: Evidence from US Labor Markets. Journal of Political Economy, 128(6), 2188-2244. doi:10.1086/705716
5. Aghion, P., Antonin, C., and Bunel, S. (2019). Artificial intelligence, growth and employment: The role of policy. Economics and Statistics, 510-512, 149 - 164. doi:10.24187/ecostat.2019.510t.1994
6. Aghion, P., Jones, B. F., and Jones, C. I. (2017). Artificial intelligence and economic growth. National Bureau of Economic Research Working Paper 23928.
7. Agrawal, A., Gans, J., and Goldfarb, A. (2019). Economic policy for artificial intelligence. Innovation Policy and the Economy, 19, 139-159. doi:10.1086/699935
8. Agrawal, A., Gans, J., and Goldfarb, A. (2019). Prediction, Judgment, and Complexity A Theory of Decision- Making and Artificial Intelligence. National Bureau of Economic Research - The Economics of Artificial Intelligence: An Agenda.

9. Aldemir, C., and Uçma Uysal, T. (2025). Artificial Intelligence for Financial Accountability and Governance in the Public Sector: Strategic Opportunities and Challenges. Administrative Sciences, 15(2), 1-19. doi: 10.3390/admsci15020058
10. Anastasopoulos, L. J., Moldogaziev, T. T., and Scott, T. (2020). Organizational context and budget orientations: a computational text analysis. International Public Management Journal, 23(2), 292-313. doi:10.1080/10967494.2019.1706677
11. Autor, D., and Salomons, A. (2017). Robocalypse Now–Does Productivity Growth Threaten Employment? Investment and growth in advanced economies, 45-118. ECB Forum on Central Banking.
12. Aydın, M. S. (2024). Kamu Hizmetlerinin Sunumunda Yapay Zeka Kullanımı. International Journal of Social, Political and Financial Researches, 4(2), 171-186. doi:10.70101/ussmad.1520892
13. Bader, J., Edwards, J., Harris-Jones, C., and Hannaford, D. (1988). Practical engineering of knowledge-based systems. Information and Software Technology, 30(5), 266-277. doi:10.1016/0950-5849(88)90019-5
14. Chu, Y.-H., Hsu, S.-M., Chang, C.-C., and Hsu, S.-H. (2020). An Economy-Wide Assessment of Artificial Intelligence Investment on Manufacturing: A Case Study of Taiwan’s Semiconductor and ICT Industries. Modern Economy, 11(5), 1040-1052. doi:10.4236/me.2020.115078
15. Corvalán, J. G. (2018). Digital and Intelligent Public Administration: transformations in the era of artificial intelligence. A&C - Administrative & Constitutional Law Review, 18(71), 55-87. doi:10.21056/aec.v18i71.857
16. Duan, Y., Edwards, J. S., and Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71. doi:10.1016/j.ijinfomgt.2019.01.021
17. Edwards, J. S., Duan, Y., and Robins, P. C. (2000). An analysis of expert systems for business decision making at different levels and in different roles. European Journal of Information Systems, 9(1), 36-46. doi:10.1057/palgrave.ejis.3000344
18. Eisikovits, N., Johnson, W. C., and Markelevich, A. J. (2024). Should Accountants be Afraid of AI? Risks and Opportunities of Incorporating Artificial Intelligence into Accounting and Auditing. Accounting Horizons, 1-7. doi:10.2139/ssrn.4748690
19. Engin, Z., and Treleaven, P. (2019). Algorithmic Government: Automating Public Services and Supporting Civil Servants in using Data Science Technologies. The Computer Journal, 62(3), 448–460. doi:10.1093/comjnl/bxy082
20. Fernandez-Cortez, V., Valle-Cruz, D., and Gil-Garcia, J. (2020). Can Artificial Intelligence Help Optimize the Public Budgeting Process? Lessons about Smartness and Public Value from the Mexican Federal Government. Seventh International Conference on eDemocracy & eGovernment (ICEDEG). 312-315. IEEE. doi:10.1109/ICEDEG48599.2020.9096745
21. Gil-Garcia, J. R., Helbig, N., and Ojo, A. (2014). Being smart: Emerging technologies and innovation in the public sector. Government Information Quarterly, 31(1), 11-18. doi:https://doi.org/10.1016/j.giq.2014.09.001
22. Gillham, J., Rimmington, L., Dance, H., Verweij, G., Rao, A., Roberts, K. B., and Paich, M. (2017). The macroeconomic impact of artificial intelligence. PWC.
23. Gries, T., and Naudé, W. (2018). Artificial Intelligence, Jobs, Inequality and Productivity: Does Aggregate Demand Matter? IZA Discussion Papers No: 12005.
24. Gries, T., and Naudé, W. (2020). Artificial Intelligence, Income Distribution and Economic Growth. IZA Discussion Paper No. 13606. doi:10.2139/ssrn.3679012
25. Harsh, A., and Ichalkaranje, N. (2014). Transforming e-Government to Smart Government: A South Australian Perspective. Intelligent Computing, Communication and Devices, 9-16.
26. Hogan-Doran, D. (2017). Computer says "no": Automation, algorithms and artificial intelligence in Government decision-making. Judicial Review: Selected Conference Papers: Journal of the Judicial Commission of New South Wales, 13(3), 345-382.
27. Hollander, A. S., and Icerman, R. (1991). Capital budgeting in governments: The feasibility of Artificial Intelligence technology. Expert Systems with Applications, 3(1), 109-116. doi:10.1016/0957-4174(91)90091-R
28. Jimeno, J. F. (2019). Fewer babies and more robots: economic growth in a new era of demographic and technological changes. SERIEs, 10, 93–114. doi:10.1007/s13209-019-0190-z
29. Kassani, S. H., Kassani, P. H., Wesolowski, M. j., and Schneider, K. A. (2021). Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach. Biocybernetics and Biomedical Engineering, 41(3), 867–879. doi:10.1016/j.bbe.2021.05.013
30. Kolkman, D. (2020). The usefulness of algorithmic models in policy making. Government Information Quarterly, 37(3), 101488. doi:10.1016/j.giq.2020.101488
31. Lee, M., Hayes, D., and Maher, C. S. (2024). AI as a Budgeting Tool: Panacea or Pandora’s Box? Public Finance Journal, 1(1), 49–65. doi:10.59469/pfj.2024.6
32. Liu, H.-W., Lin, C.-F., and Chen, Y.-J. (2019). Beyond State v Loomis: artificial intelligence, government algorithmization and accountability. International Journal of Law and Information Technology, 27(2), 122–141. doi:10.1093/ijlit/eaz001
33. Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., and Lusignan, S. (2019). Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges. Primary Health Care Informatics Working Group Contribution to the Yearbook of Medical Informatics, 41 - 46. doi:10.1055/s-0039-1677901
34. Mellouli, S., Luna-Reyes, L. F., and Zhang, J. (2014). Smart government, citizen participation and open data. Information Polity, 19(1-2), 1-4. doi:10.3233/IP-140334
35. Nolan, P., and Slater, G. (2010). Visions of the future, the legacy of the past: Demystifying the weightless economy. Labor History, 51(1), 7-27. doi:10.1080/00236561003654677
36. Özdemir, H., and Yelboğa, A. (2025). Yapay Zekâ Destekli Denetimin Kamu Harcamalarındaki Rolü: Sayıştay Örneği. Sayıştay Dergisi, 36(139), 771-802. doi:https://doi.org/10.52836/sayistay.1793520
37. Phillips-Wren, G., and Jain, L. (2006). Artificial Intelligence for Decision Making. Knowledge-Based Intelligent Information and Engineering Systems, 531–536.
38. Puron-Cid, G. (2014). Factors for a successful adoption of budgetary transparency innovations: A questionnaire report of an open government initiative in Mexico. Government Information Quarterly, 31(1), 49-62. doi:10.1016/j.giq.2014.01.007
39. Santschi, D., Grau, M. C., Fehrenbacher, D. D., and Blohm, I. (2024). Artificial Intelligence to Improve Public Budgeting. Forty-Fifth International Conference on Information Systems. 1-9. ICIS.
40. Sun, T. Q., and Medaglia, R. (2019). Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368-383. doi:10.1016/j.giq.2018.09.008
41. Torben Antretter, Blohm, I., Sirén, C., Grichnik, D., Malmström, M., and Wincent, J. (2020). Do Algorithms Make Better - and Fairer - Investments Than Angel Investors? Harvard Business Review.
42. Upchurch, M. (2018). Robots and AI at work: the prospects for singularity. New Technology Work and Employment, 33(1), 205-218. doi:10.1111/ntwe.12124
43. Valle-Cruz, D. (2019). Public value of e-government services through emerging technologies. International Journal of Public Sector Management, 32(5), 530-545. doi:10.1108/IJPSM-03-2018-0072
44. Valle-Cruz, D., Criado, J., Sandoval-Almazán, R., and Ruvalcaba-Gomez, E. A. (2020). Assessing the public policy-cycle framework in the age of artificial intelligence: From agenda-setting to policy evaluation. Government Information Quarterly, 37(4), 101509. doi:10.1016/j.giq.2020.101509
45. Valle-Cruz, D., Fernandez-Cortez, V., and Gil-Garcia, J. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2), 101644. doi:10.1016/j.giq.2021.101644
46. Wirtz, B. W., Weyerer, J. C., and Geyer, C. (2018). Artificial Intelligence and the Public Sector—Applications and Challenges. International Journal of Public Administration, 42(7), 596-615. doi:10.1080/01900692.2018.1498103
47. Wirtz, B. W., Weyerer, J. C., and Sturm, B. J. (2020). The Dark Sides of Artificial Intelligence: An Integrated AI Governance Framework for Public Administration. International Journal of Public Administration, 43(9), 818-829. doi:10.1080/01900692.2020.1749851
48. Xu, M., David, J. M., and Kim, S. H. (2018). The fourth industrial revolution: Opportunities and challenges. International Journal of Financial Research, 9(2), 13194-13194. doi:10.5430/ijfr.v9n2p90
49. Yavuz, E., and Özgül, H. B. (2025). The Role of AI in Public Budget Processes: A Comparative Evaluation on National AI Strategies and Practical Examples. Sayıştay Dergisi, 36(138), 575-602. doi:10.52836/sayistay.1717763
50. Yetkin Ataer, M. (2025). Yapay Zeka ve Türkiye’de Kamu Maliyesinin Değişen Rolü. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34(Özel Sayı: Uygarlığın Dönüşümü: Yapay Zekâ), 341-354. doi:10.35379/cusosbil.1696963
51. Zhu, J., Zhang, J., and Feng, Y. (2022). Hard budget constraints and artificial intelligence technology. Technological Forecasting and Social Change, 183, 1-12. doi:10.1016/j.techfore.2022.121889