Auditing Complex Estimates: How Do Construal Level and Evidence Formatting Impact Auditors' Consideration of Inconsistent Evidence?
Bibliographic record
Abstract
ABSTRACT The Public Company Accounting Oversight Board is concerned about auditors' tendency to ignore relevant information that is inconsistent with management's assumptions underlying complex estimates. We find that priming auditors to consider how management arrived at a particular assumption helps curb aggressive reporting by encouraging auditors to engage in low‐level, concrete thinking regarding the direct evidence underlying the assumption. Low‐level, concrete thinking enhances auditors' sensitivity to relevant contradictory evidence. We also find that auditors reviewing graphical (versus textual) evidence are more skeptical of aggressive assumptions underlying a complex estimate. Evidence suggests that this is because graphs provide a better cognitive fit for tasks requiring comparisons and associations among data points. Our study is important to practitioners, regulators, and researchers as it sheds light on how a simple prime and the presentation format of audit evidence influence auditors' professional skepticism in this area. Additionally, it supports audit firms' initiatives to transform data to more visual formats by highlighting a context in which graphs improve auditors' judgments. Finally, we provide evidence as to how different primes affect auditors' evaluation of evidence, which can be useful in designing more effective audit plans.
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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 | 0.050 | 0.365 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".