Agentic AI and Human-AI Collaboration in Auditing: Reframing Roles, Accountability, and Governance
Bibliographic record
Abstract
Artificial intelligence has been discussed in auditing as a set of tools designed to enhance efficiency, expand analytical capacity, and support auditors’ professional judgment. A substantial body of accounting information systems (AIS) research has emphasized that technological advances reshape audit processes without displacing the central role of professional judgment (e.g., Vasarhelyi et al., 2004; Kokina et al., 2025). Advances in large language models and multi-agent systems, however, mark a departure from this traditional framing. These developments have given rise to agentic AI, which refers to AI systems capable of decomposing objectives, initiating actions, coordinating subtasks, and iteratively refining outputs. As a result, agentic AI raises qualitatively different questions from earlier generations of audit analytics or computer-assisted audit techniques.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.029 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".