Mind the Gap: Why Do Experts Have Differences of Opinion Regarding the Sufficiency of Audit Evidence Supporting Complex Fair Value Measurements?
Bibliographic record
Abstract
ABSTRACT Reported deficiencies continue to persist in audits of fair value measurements and other complex accounting estimates (hereafter, “FVMs”), despite improvements in auditor performance observed by regulators. The persistence of reported deficiencies in audits of FVMs suggests that factors underlying this trend may be more complicated and multidimensional than previously suggested by regulators and academic research, which has focused largely on auditors' unsatisfactory performance as the principal source of reported deficiencies. Drawing from the judgment and decision‐making expertise literature, we gather field‐based data from audit experts to identify additional factors that are likely to be contributing to differences of opinion between audit and inspection experts and the persistence of reported deficiencies in audits of FVMs. We find evidence that audit experts interpret standards and evaluate audit evidence differently than inspectors, and thus perceive there to be a gap between what auditors and inspectors regard as sufficient appropriate audit evidence to support audits of FVMs (hereafter, “FVM gap”). Moreover, results highlight several areas in audits of FVMs where differences of opinion exist between auditor and inspector experts regarding what constitutes a reported deficiency. Within the contexts we examine, our results identify additional factors, beyond deficient auditor performance, that may contribute to the FVM gap. We also report audit partners' recommendations for ways to reduce the FVM gap and suggest avenues for future research. Gaining a more complete understanding of sources contributing to reported deficiencies will help regulators, standard setters, audit firms, and academics to identify ways to reduce the FVM gap and reported deficiencies in audits of FVMs.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".