Auditors’ Professional Skepticism: Neutrality versus Presumptive Doubt
Bibliographic record
Abstract
Although skepticism is widely viewed as essential to audit quality, there is a debate about what form is optimal. The two prevailing perspectives that have surfaced are “neutrality” and “presumptive doubt.” With neutrality, auditors neither believe nor disbelieve client management. With presumptive doubt, auditors assume some level of dishonesty by management, unless evidence indicates otherwise. The purpose of this study is to examine which of these perspectives is most descriptive of auditors’ skeptical judgments and decisions, in higher and lower control environment risk settings. This issue is important, since there is a lack of empirical evidence as to which perspective is optimal in addressing client risks. An experimental study is conducted involving a sample of 96 auditors from one of the Big 4 auditing firms in the Netherlands, with experience ranging from senior to partner. One of the skepticism measures is reflective of neutrality, the Hurtt Professional Skepticism Scale ( HPSS ), whereas the other reflects presumptive doubt, the inverse of the Rotter Interpersonal Trust Scale ( RIT ). The findings suggest that the presumptive doubt perspective of professional skepticism is more predictive of auditor skeptical judgments and decisions than neutrality, particularly in higher‐risk settings. Since auditing standards prescribe greater skepticism in higher‐risk settings, the findings support the appropriateness of a presumptive doubt perspective and have important implications for auditor recruitment and training, guidance in audit tools, and future research.
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How this classification was reachedexpand
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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; both teacher heads agree on what is shown here.
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".