Walking the Talk? Managing Errors in the Audit Profession*
Bibliographic record
Abstract
ABSTRACT Errors reflect unintended deviations from plans or goals and commonly carry negative connotations. Although errors cannot be eliminated, they offer opportunities for learning and innovation. Audit firms employ powerful mechanisms, such as review processes, to prevent or detect audit errors and safeguard their work in the public interest. At the same time, the profession recognizes positive long‐term outcomes of errors in terms of continuous learning to enhance auditor skills and, ultimately, audit quality. The current study employs semistructured interviews with Dutch auditors to investigate how they manage the tensions emanating from extant public and regulatory demands for flawless audits while embracing errors as opportunities for learning. Our findings reveal that auditors express a positive attitude toward openly communicating audit errors, but, in substance, they espouse negative emotions and defensive strategies for fear of repercussions. We argue that the excessive emphasis audit firms and oversight bodies place on error prevention conditions auditors into perceiving errors as negative and avoidable events. We assert these attitudes result from the profession's efforts to maintain status and legitimacy in the eyes of the public and the regulator, where any auditor error may shed doubt on auditors' work in the public interest. In sum, our findings indicate that viewing errors as incompatible with audit work makes the profession susceptible not only to repeating errors but also to missing out on opportunities to improve services and to achieve innovation.
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 | 0.016 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".