Auditor judgment in the fourth industrial revolution
Bibliographic record
Abstract
Abstract Discourse proclaiming the advent of a fourth industrial revolution predicts significant disruption to various work domains in the near future. Auditing is one of the domains where bold claims about the potential of technology are being made, with technology expected to augment auditors' judgments and, in time, possibly automate them. Drawing on 44 in‐depth interviews with auditors, regulators, and emergent artificial intelligence software providers, we question the prevailing narrative around technological change in auditing which suggests that ostensibly simple, low‐level technical tasks are areas where little judgment is at play and thus are ripe for automation. We show that significant elements of deliberation, sensemaking, and reflexivity, arguably critical for the socialization of early career auditors into the profession, may be lost when automating areas of work perceived as low value, leading us to question what it means to apply judgment in auditing. Conversely, higher‐level aspects of the audit process may be assisted by technology and augmented in different ways, yet new technological structures generate new areas of indeterminacy that pose new and yet unresolved demands on auditors' judgment. Overall, the paper shows how auditor habits are changing and highlights the risks posed by new technologies to the acquisition of practical knowledge by auditors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".