The Effects of Accounting Standard Precision, Auditor Task Expertise, and Judgment Frameworks on Audit Firm Litigation Exposure
Bibliographic record
Abstract
Abstract Recent research suggests that adopting imprecise accounting standards elevates audit firm litigation exposure and could undermine auditor objectivity if audit firms respond by herding to industry norms. This paper reports the results of two experiments that demonstrate how audit firms can effectively mitigate the elevated litigation exposure without herding to industry norms by staffing engagements with recognized technical experts, using judgment frameworks and automated decision aids, and providing persuasive evidence of adherence to auditing standards. We find that judgment frameworks are particularly well‐suited for defending judgments under imprecise standards, and represent a cost‐effective alternative to using technical experts. However, our results also indicate that judgment frameworks may provide a safe harbor for relatively low‐quality judgments when those frameworks are used under precise standards. We discuss implications for audit firms, courts, and regulators that currently conduct or evaluate audits within and across jurisdictions where the precision of accounting standards varies considerably.
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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 | 0.046 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".