Improving Auditors' Consideration of Evidence Contradicting Management's Estimate Assumptions
Bibliographic record
Abstract
ABSTRACT Auditors have difficulty evaluating the assumptions underlying management's estimates. One source of these problems is that auditors appear to dismiss evidence contradicting management's assumptions because their initial preference to support management's accounting biases their preliminary conclusions and, thus, their interpretation of evidence. We experimentally examine whether auditors with a balanced focus (i.e., a focus on documenting evidence that supports and contradicts their preliminary conclusion) are less likely to dismiss evidence that contradicts management's assumptions than auditors with a supporting focus (i.e., a focus on documenting evidence that supports their preliminary conclusion). We expect and find that, compared with auditors with a supporting focus, auditors with a balanced focus create documentation that is less dismissive of evidence contradicting management's estimate. Importantly, a balanced focus changes auditors' cognition and affects how auditors interpret contradicting evidence rather than merely increasing their documentation of this evidence. The effects of reduced dismissiveness persist to improve auditors' evaluations of a biased estimate and subsequent actions, improving audit quality in an important and difficult area.
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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 | 0.113 | 0.432 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".