The Effects of High Estimate Uncertainty in Auditor Negligence Litigation*
Bibliographic record
Abstract
We examine how jurors' negligence judgments and attorneys' out‐of‐court settlements are differently impacted by two features of a materially misstated accounting estimate—the amount of estimate uncertainty and whether the misstated account is disaggregated into its own line‐item or aggregated with other accounts into a single financial statement line‐item. We predict and find that jurors and attorneys react to estimate uncertainty in opposite directions under common conditions. This finding is important because when jurors' judgments and attorneys' settlements differ, research into juror judgments alone may not capture a complete picture of auditor liability because the vast majority of audit litigation is resolved by attorneys in out‐of‐court settlement without ever going to trial. Consistent with attribution theory, results from our first experiment show that jurors hold auditors more responsible for misstatements of lower estimate uncertainty when the misstated account is disaggregated, as opposed to misstatements that are of higher uncertainty and/or aggregated with other, accurate accounts. However, in a second experiment we find that attorneys negotiate auditor settlements under the incorrect assumption that jurors will hold auditors more responsible for failing to prevent misstatements of higher uncertainty. Our results illustrate that accounting research should not focus solely on juror judgments in the study of how specific factors impact auditor liability, and that attorneys would benefit from a better understanding of juror decision making.
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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 | 0.015 | 0.163 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".