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

Abstract

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.

Keywords

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

Supporting Institution

Yazar bu çalışma için finansal destek almadığını beyan etmiştir.

Ethical Statement

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

Abstract

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.

Keywords

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

Supporting Institution

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

Ethical Statement

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

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