Auditing Complex Estimates: How Do Construal Level and Evidence Formatting Impact Auditors' Consideration of Inconsistent Evidence?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.027 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it