Agentic AI and Human–AI Collaboration in Auditing: Roles, Benefits, and Risks in the Korean Audit Market
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>Artificial intelligence (AI) is rapidly transforming the auditing profession (Dong et al. 2023; Gu et al. 2024). Early pplications primarily focused on automating routine tasks, such as journal entry testing or anomaly detection. More recently, advances in AI have given rise to agentic AI—systems capable of autonomous goal setting, iterative reasoning, and adaptive task execution (Li et al. 2025). Unlike traditional decision-support tools, agentic AI can actively participate in audit processes by identifying risks, proposing procedures, and generating documentation. This shift raises fundamental questions regarding auditor responsibility, professional judgment, and accountability. These issues are particularly salient in jurisdictions with strong regulatory oversight and high legal exposure for auditors. The Korean audit market provides a distinctive and informative setting in this regard. Korea is characterized by stringent auditor liability under the External Audit Act, intensive regulatory inspections by the Financial Supervisory Service (FSS), and a growing emphasis on audit process documentation and consistency. This commentary examines how agentic AI may be integrated into auditing through a human–AI collaboration framework, focusing on the roles, benefits, and risks of such collaboration in the Korean audit environment.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it