Can Audit Committee Expertise Increase External Auditors' Litigation Risk? The Moderating Effect of Audit Committee Independence
Bibliographic record
Abstract
ABSTRACT This study examines whether the perceived independence and financial expertise of audit committee members affect external auditors' exposure to legal liability. We use an experiment in which potential jurors make judgments about auditor independence and legal liability for a case involving an audit failure. We find that perceptions of audit committee independence from management are positively associated with judgments of auditor independence and negatively associated with auditor liability. However, financial expertise of audit committee members can be a double‐edged sword. Our experiment finds that judgments of auditor liability are higher when the audit committee is perceived to have higher financial expertise but lower independence from management. In assessing litigation risk of current and prospective clients, auditors may want to carefully consider the independence of audit committee members from management, particularly when audit committee members have financial expertise. In the event of an audit failure, the financial expertise of nonindependent audit committee members can negatively affect jurors' perceptions of auditor independence and liability.
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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 | 0.011 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".