Bağımsız Denetimde Yapay Zekâ Destekli Kanıt Değerlendirme Modeli: İnsan Merkezli Kavramsal Bir Öneri

Öz

Bu çalışmada, yapay zekâ (YZ) tabanlı araçlar tarafından işlenen veya üretilen bilgilerin bağımsız denetim sürecinde hangi koşullar altında kullanılabileceğini açıklamak amacıyla; Bağımsız Denetim Standartlarını (BDS) sosyo-teknik sistemler teorisi, insan-merkezli YZ ve hibrit zekâ yaklaşımlarıyla bütünleştiren İnsan-Merkezli Hibrit Kanıt Değerlendirme Modeli önerilmektedir. Geliştirilen model; verinin elde edilmesi ve hazırlanması, ön analiz ve risk sinyallerinin üretilmesi, YZ tarafından işlenen veya üretilen bilgilerin denetim kanıtı niteliğinin değerlendirilmesi ile kanıtların bütüncül değerlendirilerek denetim görüşünün oluşturulması olmak üzere dört basamaktan oluşmaktadır. Her basamak sonunda yer alan karar noktaları ve geri besleme yapısı, denetçinin ilave prosedür uygulamasına, önceki basamaklara dönmesine veya çıktıyı kullanmamasına olanak sağlamaktadır. Model, YZ destekli bilgilerin ihtiyaca uygunluk, güvenilirlik, veri bütünlüğü, metodolojik uygunluk, doğrulanabilirlik, diğer kanıtlarla tutarlılık, çelişkili kanıtların çözümlenmesi ve yeterlilik bakımından değerlendirilmesini gerektirmektedir. Çalışma, YZ kullanımını kademeli ve geri beslemeli bir kanıt değerlendirme süreci içinde konumlandırmakta; nihai mesleki yargı, sorumluluk ve hesap verebilirliğin denetçide kalması gerektiğini ortaya koymaktadır.

Anahtar Kelimeler

• Yapay Zekâ
• Denetim Kanıtı
• Mesleki Yargı
• Mesleki Şüphecilik
• İnsan Merkezli Yapay Zekâ
• Bağımsız Denetim

Destekleyen Kurum

Yazar bu çalışma için finansal destek almadığını beyan etmiştir.

Etik Beyan

Bu makale, insan veya hayvanlar ile ilgili etik onay gerektiren herhangi bir araştırma içermemektedir.

An AI-Assisted Evidence Evaluation Model for Independent Auditing: A Human-Centered Conceptual Framework

Öz

This study proposes a Human-Centered Hybrid Evidence Evaluation Model that specifies the conditions under which information processed or generated by artificial intelligence (AI) tools can be used in independent auditing. The model, which integrates the Turkish Standards on Auditing with socio-technical systems theory, human-centered AI, and hybrid intelligence, consists of four stages: obtaining and preparing data, conducting preliminary analyses and generating risk signals, assessing whether AI-processed or AI-generated information can be used as audit evidence, and evaluating audit evidence as a whole to form the audit opinion. Decision points and feedback mechanisms at the end of each stage allow the auditor to perform additional procedures, return to earlier stages, or discontinue the use of an output. The model requires AI-processed/generated information to be evaluated in terms of relevance, reliability, data integrity, methodological suitability, verifiability, consistency with other evidence, resolution of contradictory evidence, and sufficiency. By positioning AI use within a staged and iterative evidence evaluation process, the study presents a framework in which AI supports rather than replaces professional judgment, while final professional judgment, professional responsibility, and accountability remain with the auditor.

Anahtar Kelimeler

• Artificial Intelligence
• Audit Evidence
• Professional Judgment
• Professional Skepticism
• Human-Centered Artificial Intelligence
• Independent Auditing

Destekleyen Kurum

The author received no financial support for the research, authorship and/or publication of this article.

Etik Beyan

This article does not contain any studies with human participants or animals performed by the authors.

Kaynakça

1. Abdolmohammadi, M. J. (1987). Decision support and expert systems in auditing: A review and research directions. Accounting and Business Research, 17(66), 173–185.
2. Akata, Z., Balliet, D., de Rijke, M., Dignum, F., Dignum, V., Eiben, G., Fokkens, A., Grossi, D., Hindriks, K., Hoos, H., Hung, H., Jonker, C., Monz, C., Neerincx, M., Oliehoek, F., Prakken, H., Schlobach, S., van der Gaag, L., van Harmelen, F., & Welling, M. (2020). A research agenda for hybrid intelligence: Augmenting human intellect with collaborative, adaptive, responsible, and explainable artificial intelligence. Computer, 53(8), 18–28. https://doi.org/10.1109/MC.2020.2996587
3. Appelbaum, D. (2016). Securing big data provenance for auditors: The big data provenance black box as reliable evidence. Journal Of Emerging Technologies in Accounting, 13(1), 17-36.
4. Bainbridge, L. (1982). Ironies of automation. IFAC Proceedings Volumes, 15(6), 129-135.
5. Baxter, G., & Sommerville, I. (2011). Socio-technical systems: From design methods to systems engineering. Interacting with Computers, 23(1), 4–17.
6. Bostrom, R., & Heinen, J. (1977). MIS problems and failures: A socio-technical perspective PART 1: The causes. Management Information Systems Quarterly, 1(1), 9.
7. Brown-Liburd, H., Issa, H., & Lombardi, D. (2015). Behavioral implications of big data’s impact on audit judgment and decision making and future research directions. Accounting Horizons, 29(2), 451–468.
8. Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W. W. Norton & Company.

9. Cherns, A. (1976). The principles of sociotechnical design. Human Relations, 29(8), 783–792.
10. Commerford, B. P., Dennis, S. A., Joe, J. R., & Ulla, J. W. (2022). Man versus machine: Complex estimates and auditor reliance on artificial intelligence. Journal of Accounting Research, 60(1), 171–201.
11. Commerford, B. P., Eilifsen, A., Hatfield, R. C., & Holmstrom, K. M. (2024). Control issues: How providing input affects auditors' reliance on artificial intelligence. Contemporary Accounting Research. https://doi.org/10.1111/1911-3846.12974
12. Cvetkovic, I. (2025). Human-centered hybrid intelligence: Provision of human input and its utilization by AI in creative work and problem-solving [Doktora tezi, Universität Hamburg].
13. Dede, A. (2024). Yapay zekâ teknolojilerinin bağımsız denetim uygulamalarına etkileri. Premium e-Journal of Social Sciences, 8(46), 1210–1221.
14. Dellermann, D., Ebel, P., Söllner, M., & Leimeister, J. M. (2019). Hybrid intelligence. Business & Information Systems Engineering, 61(5), 637–643.
15. Duan, H. K., & Vasarhelyi, M. A. (2023). Challenges for leveraging explainable artificial intelligence in audit procedures. Journal of Emerging Technologies in Accounting, 23(1), 59-70.
16. Efe, A. & Tunçbilek, M. (2023). Yapay zekâ algoritmaları ile dönüşen denetim araçları üzerine bir değerlendirme. Denetişim, 27(14), 72-102.
17. European Union. (2024). Regulation (EU) 2024/1689 of the European Parliament and of the Council on artificial intelligence (AI Act).
18. Fedyk, A., Hodson, J., Khimich, N., & Fedyk, T. (2022). Is artificial intelligence improving the audit process? Review of Accounting Studies, 27, 938–985.
19. Fırat, Z. (2025). Artificial intelligence in auditing: Opportunities, challenges, and future directions. Muhasebe Bilim Dünyası Dergisi, 27(1), 77–95.
20. Föhr, T. L. (2024). A method to categorize and classify artificial intelligence applicable to the risk-based audit approach. Die Unternehmung, 78(3), 264-310.
21. Gu, H., Schreyer, M., Moffitt, K., & Vasarhelyi, M. A. (2024). Artificial intelligence co-piloted auditing. International Journal of Accounting Information Systems, 54, 100698.
22. Hirosawa, T., Suzuki, T., Shiraishi, T., Hayashi, A., Fujii, Y., Harada, T., & Shimizu, T. (2024). Adapting artificial intelligence concepts to enhance clinical decision-making: A hybrid intelligence framework. International Journal of General Medicine, 17, 5417–5422. https://doi.org/10.2147/IJGM.S497753
23. Hunton, J. E., & Rose, J. M. (2010). 21st century auditing: Advancing decision support systems to achieve continuous auditing. Accounting Horizons, 24(2), 297–312.
24. Hurtt, R. K. (2010). Development of a scale to measure professional skepticism. Auditing: A Journal of Practice & Theory, 29(1), 149–171.
25. IAASB. (2024). The International Auditing and Assurance Standards Board. Teknoloji konum çerçevesi (Technology position). International Federation of Accountants. Web: https://ifacweb.blob.core.windows.net/publicfiles/2024-10/IAASB-Technology-Position-Statement.pdf
26. IAASB. Technology catalog of issues and possible actions. Web: https://www.iaasb.org/publications/technology-catalog-issues-and-possible-actions
27. Jarrahi, M. H., Lutz, C., & Newlands, G. (2022). Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation. Big Data & Society, 9(2), 1–6. https://doi.org/10.1177/20539517221142824
28. Kantarcı, N., & Uzay, Ş. (2025). Yapay zekâ, blok zinciri ve diğer yeni teknolojilerin hileli finansal raporlamanın tespitine etkisi: Nitel bir analiz. Muhasebe ve Finansman Dergisi, (107), 1–28.
29. Karagün, V. (2018). Denetimde kullanılan dijital analiz teknikleri ve çok basamaklı yapay sinir ağları ile denetim planlamasında bir model önerisi (Yüksek lisans tezi). Bursa Uludağ Üniversitesi, Sosyal Bilimler Enstitüsü.
30. KGK. Türkiye Denetim Standartları 2026 Seti. Web: https://www.kgk.gov.tr/DynamicContentDetail/12046/TDS-2026-Seti
31. Knechel, W. R. (2000). Behavioral research in auditing and its impact on audit education. Issues in Accounting Education, 15(4), 695–712.
32. Knechel, W. R., Krishnan, G. V., Pevzner, M., Shefchik, L. B., & Velury, U. K. (2013). Audit quality: Insights from the academic literature. Auditing: A Journal of Practice & Theory, 32(Supplement 1), 385–421.
33. Kokina, J., & Davenport, T. H. (2017). The emergence of artificial intelligence: How automation is changing auditing. Journal of Emerging Technologies in Accounting, 14(1), 115–122.
34. Kokina, J., Blanchette, S., Davenport, T. H., & Pachamanova, D. (2025). Challenges and opportunities for artificial intelligence in auditing: Evidence from the field. International Journal of Accounting Information Systems, 56, 100734.
35. Küster, S., Steindl, T., & Göttsche, M. (2025). The informational content of key audit matters: Evidence from using artificial intelligence in textual analysis. Contemporary Accounting Research. 42(4), 2392-2423.
36. Lee, E. J., & Tahmoush, D. (2025). Auditors' decision-making aid for going concern audit opinions through machine learning analysis. International Journal of Accounting Information Systems, 56, 100732.
37. Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50-80.
38. Leocádio, D., Malheiro, L., & Reis, J. (2024). Artificial intelligence in auditing: A conceptual framework for auditing practices. Administrative Sciences, 14, 238.
39. Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459-9474.
40. Li, H., de Freitas, M. M., Lee, H., & Vasarhelyi, M. A. (2024). Enhancing continuous auditing with large language models: AI-assisted real-time accounting information cross-verification. SSRN. https://ssrn.com/abstract=4692960
41. Li, Y., & Goel, S. (2025). Artificial intelligence auditability and auditor readiness for auditing artificial intelligence systems. International Journal of Accounting Information Systems, 56, 100739.
42. Mala, R., & Chand, P. (2015). Judgment and decision-making research in auditing and accounting: Future research implications of person, task, and environment perspective. Accounting Perspectives, 14(1), 1–50.
43. Munoko, I., Brown-Liburd, H. L., & Vasarhelyi, M. (2020). The ethical implications of using artificial intelligence in auditing. Journal of Business Ethics, 167, 209–234.
44. Murray, A., Rhymer, J. E. N., & Sirmon, D. G. (2021). Humans and technology: Forms of conjoined agency in organizations. Academy of Management Review, 46(3), 552-571.
45. Nelson, M. W. (2009). A model and literature review of professional skepticism in auditing. Auditing: A Journal of Practice & Theory, 28(2), 1–34.
46. NIST. (2023). Artificial intelligence risk management framework (AI RMF 1.0). National Institute of Standards and Technology. Web: https://nvlpubs.nist.gov/nistpubs/ai/nist.ai.100-1.pdf
47. Omoteso, K. (2012). The application of artificial intelligence in auditing: Looking back to the future. Expert Systems with Applications, 39, 8490–8495.
48. Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 30(3), 286-297.
49. Raisch, S., & Fomina, K. (2025). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2), 441–464. https://doi.org/10.5465/amr.2021.0421
50. Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.
51. Salijeni, G., Samsonova-Taddei, A., & Turley, S. (2019). Big data and changes in audit technology: Contemplating a research agenda. Accounting and Business Research, 49(1), 95–119.
52. Santoni de Sio, F., & Van den Hoven, J. (2018). Meaningful human control over autonomous systems: A philosophical account. Frontiers in Robotics and AI, 5, 323836.
53. Sauer, C. R., & Burggräf, P. (2025). Hybrid intelligence – Systematic approach and framework to determine the level of human-AI collaboration for production management use cases. Production Engineering, 19(3), 525–541. https://doi.org/10.1007/s11740-024-01326-7
54. Seethamraju, R. (2025). AI use in auditing – A technology dominance perspective. Proceedings of the 58th Hawaii International Conference on System Sciences (HICSS).
55. Seethamraju, R., & Hecimovic, A. (2022). Adoption of artificial intelligence in auditing: An exploratory study. Australian Journal of Management, 48(4), 780-800.
56. Sheldon, M. D. (2026). Auditing Artificial Intelligence: Context Engineering with Retrieval-Augmented Generation. Journal of Information Systems, 1-27.
57. Shneiderman, B. (2020). Human-centered artificial intelligence: Reliable, safe & trustworthy. International Journal of Human–Computer Interaction, 36(6), 495–504.
58. Siddiqui, O., & Raheman, A. (2026). Exploring the role of technology in auditing: Opportunities and challenges from stakeholder perspectives. International Journal of Auditing, 30(2), 337-353.
59. Singh, N., Lai, K., Vejvar, M., & Cheng, T. C. E. (2019). Data-driven auditing: A predictive modeling approach to fraud detection and classification. Journal of Corporate Accounting & Finance, 30(3), 64-82.
60. Srivastava, R. P., Dutta, S. K., & Johns, R. W. (1996). An expert system approach to audit planning and evaluation in the belief-function framework. International Journal of Intelligent Systems in Accounting, Finance and Management, 5(3), 165-183.
61. Sterman, J. (2002). System Dynamics: Systems thinking and modeling for a complex world. Working Paper, Massachusetts Institute of Technology Engineering Systems Division.
62. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56.
63. Trist, E. L., & Bamforth, K. W. (1951). Some social and psychological consequences of the longwall method of coal-getting. Human Relations, 4(1), 3–38.
64. Tsougkas, N. (2026). AI Applications in Auditing and IFRS Compliance: A Critical Literature Review, SSRN: https://ssrn.com/abstract=6528298. 
65. Vasarhelyi, Miklos A., Kogan, Alexander, & Tuttle Brad M. (2015). Big data in accounting: An overview. Accounting Horizons, 29 (2), 381–396.
66. Xu, W. (2019). Toward human-centered AI: A perspective from human-computer interaction. Interactions, 26(4), 42–46.
67. Yang, R. (2025). Research on the application of artificial intelligence in audit risk assessment. Financial Strategy and Management Reviews, 1(1), 1000046-1000046.
68. Zhang, C. A., Cho, S., & Vasarhelyi, M. (2022). Explainable artificial intelligence (XAI) in auditing. International Journal of Accounting Information Systems, 46, 100572.
69. Zheng, N.-N., Liu, Z.-Y., Ren, P.-J., Ma, Y.-Q., Chen, S.-T., Yu, S.-Y., & Wang, F.-Y. (2017). Hybrid-augmented intelligence: Collaboration and cognition. Frontiers of Information Technology & Electronic Engineering, 18(2), 153–179. https://doi.org/10.1631/FITEE.1700053
70. Zhong, C. (2024). Transparent AI in auditing through explainable AI. Current Issues in Auditing, 18(2), A1–A14.