(untitled) (gjstudies, 2021)
This research investigates the complex interplay between artificial intelligence systems and humanjudgment in contemporary hybrid auditing environments. As organizations increasingly adopt AIpowered auditing tools while maintaining traditional manual analysis, understanding how these twoapproaches complement and potentially conflict becomes crucial for audit quality and effectiveness. Ourstudy employs a novel methodological framework combining experimental simulations with qualitativeanalysis of auditor decision-making processes across 15 financial institutions. We developed a uniqueassessment protocol that measures judgment calibration, cognitive bias mitigation, and decision confidence in scenarios where AI recommendations either align with or contradict human intuition. Thefindings reveal several counterintuitive patterns: human auditors demonstrated superior judgment indetecting novel fraud patterns that fell outside AI training datasets, while AI systems excelled at identifying subtle statistical anomalies across large transaction volumes. However, the most significant findingconcerns the ’validation paradox’—auditors showed decreased scrutiny of AI-generated findings whenthey aligned with initial hypotheses, potentially creating new blind spots. Our research contributes tothe emerging literature on human-AI collaboration in professional settings by proposing a dynamic calibration model that optimizes the allocation of auditing tasks between human and artificial intelligencebased on problem characteristics, data quality, and risk assessment. This study addresses a criticalgap in understanding how professional judgment evolves in increasingly automated environments andprovides practical frameworks for organizations seeking to implement hybrid auditing systems withoutcompromising audit quality or professional skepticism.