Do Auditors Accurately Predict Litigation and Reputation Consequences of Inaccurate Accounting Estimates?
Bibliographic record
Abstract
ABSTRACT To effectively manage audit risk, auditors must correctly predict the potential litigation and reputation consequences associated with inaccurate accounting estimates. Accurate predictions are critical because underestimation of negative consequences leads to excess legal exposure and overestimation leads to overauditing. Our paper examines whether auditors correctly anticipate these litigation and reputation outcomes. We provide manager‐ and partner‐level auditors with case facts from an auditor negligence lawsuit and ask them to predict the proportion of juries that will return verdicts against their firm. We then compare auditors' predictions to the actual verdicts we observe when we provide the same set of case facts to mock jurors who deliberate as part of juries. We find that auditors overestimate the likelihood of negligence verdicts, especially when audit quality is relatively high. Our supplemental measures help explain the reasons for this overestimation: auditors tend to underestimate jurors' perceptions of audit quality and willingness to attribute inaccurate estimates to situational factors. Finally, we examine auditors' predictions about how a news article about the litigation will affect their reputation with the general public. Similar to our litigation results, we find that auditors tend to overestimate the article's negative impact on auditor reputation. Collectively, our findings suggest that auditors overestimate litigation and reputation consequences resulting from inaccurate accounting estimates. This overestimation is consequential as it leads to inefficient allocation of audit resources.
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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 | 0.018 | 0.158 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".