The Settlement Norm in Audit Legal Disputes: Insights from Prominent Attorneys
Bibliographic record
Abstract
ABSTRACT Prior research indicates that most audit legal disputes settle. There is, however, little evidence of the factors that drive the settlement norm and its exceptions in audit legal disputes. To better understand these factors, we rely on theory related to how professionals manage risks and, as a result, how professions defend jurisdictional claims. We use this theoretical lens to help motivate four research questions that we probe by interviewing 27 prominent attorneys experienced in audit litigation. Consistent with our lens, our interview data indicate that attorneys manage their risks, including the risk of reputational loss, by settling based on their expectations of trial verdicts. Unlike trials, settlements simultaneously enable attorneys on both sides to limit costs and avoid catastrophic jury verdicts and, by doing so, claim “wins” for their clients. Attorneys also stress that they settle many audit disputes without any legal filings. Thus, a large subset of disputes is invisible to the public and researchers. Attorneys characterize trials as exceptions to the settlement norm that emerge due to abnormal conditions sometimes present in disputes. However, trial verdicts in these abnormal conditions help attorneys justify the use of settlements to clients, as attorneys stress that by settling they can avoid the dreaded possibility of extreme unfavorable verdicts. We conclude that as individual attorneys manage their risks, especially the risk of reputational loss, their profession maintains its public image and thereby defends its jurisdictional claims. Among the many questions we pose for future research is whether the settlement norm reduces society's ability to monitor the audit profession and, more generally, whether this norm's benefits outweigh its drawbacks.
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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 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".