YAPAY ZEKÂ ÇAĞINDA MESLEKİ ŞÜPHECİLİK

Abstract

Yapay zekânın araştırma ve fikir üretimi, belge özetleme ve rapor hazırlama gibi denetim süreçlerine giderek artan biçimde entegre edilmesi, mesleki şüpheciliğin nasıl korunabileceği sorusunu gündeme getirmektedir. Literatür taramasına dayanan bu çalışma, yapay zekânın mesleki şüpheciliği bağımsız biçimde uygulayıp uygulayamayacağını sorgulamakta ve yapay zekâ destekli denetimde karşılaşılan dört temel zorluğu ele almaktadır: veriye bağımlılık, şeffaflık ve açıklanabilirlik eksikliği, otomasyon önyargısı ve yapay zekânın denetlenebilirliği. Bulgular, yapay zekâ uygulamalarının nesnellik, mahremiyet, şeffaflık, hesap verebilirlik ve güvenilirlik ilkelerine uygun hale gelebilmesi için test edilebilir ve ölçülebilir standartların geliştirilmesi, denetçilerin yapay zekâ okuryazarlığının artırılması ve düzenleyiciler, akademi ile uygulayıcılar arasında disiplinler arası koordinasyonun sağlanması gerektiğini göstermektedir. Bu çalışma, mesleki şüpheciliğin salt bir veri işleme becerisi değil, muhakeme, bağlamsal değerlendirme ve etik sorumluluk içeren insan merkezli bir yetkinlik olduğunu, bu nedenle yapay zekânın denetimde destekleyici bir araç olarak konumlandırılması gerektiğini ve bilinçli ve sorumlu biçimde kullanıldığında şüpheciliği güçlendirebileceğini vurgulamaktadır

Keywords

• Mesleki Şüphecilik
• Yapay Zekâ
• Denetim
• Algoritmik Yargı

PROFESSIONAL SKEPTICISM IN THE ARTIFICIAL INTELLIGENCE ERA

Abstract

The increasing integration of artificial intelligence into audit processes including research and idea generation, document summarization, and report preparation raises the question of how professional skepticism can be preserved. Based on literature review, this study interrogates whether AI can independently exercise professional skepticism and identifies four major challenges in AI-assisted auditing: data dependence, insufficient transparency and explainability, automation bias, and constraints on AI auditability. The findings indicate a need for testable and measurable standards, enhanced AI literacy among auditors, and strengthened interdisciplinary coordination among regulators, academics, and practitioners to align AI applications with the principles of objectivity, privacy, transparency, accountability, and reliability. This study emphasizes that professional skepticism is not just a data-processing skill but, more importantly, a human-centered competence involving professional judgment, contextual assessment, and ethical responsibility. Accordingly, AI should be positioned as a supportive tool in auditing that, when used consciously and responsibly, can strengthen professional skepticism.

Keywords

• : Professional Skepticism
• Artificial Intelligence
• Auditing

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