Auditor Responsibility in the Context of Forward-Looking Judgments, Fraud Risk, and Evolving Stakeholder Expectations
Bibliographic record
Abstract
This paper examines the changing scope of auditor responsibility in light of growing reliance on forward-looking estimates, fraud risk, and stakeholder expectations. It critiques the traditional Audit Risk Model, which assumes risks are quantifiable and independent, and shows how it fails to address uncertainties in areas such as expected credit losses, goodwill impairment, and fair value assessments. In response, newer frameworks like the Performance Materiality Model offer a more integrated approach by combining audit risk with accounting risk, better aligning with the complexities of modern audits. The discussion also highlights the audit expectation gap, which is the disparity between what the public expects auditors to do and what auditing standards require. Although stakeholders increasingly expect auditors to detect fraud and anticipate business failure, the auditor’s formal role remains limited to providing reasonable assurance. Professional skepticism, though crucial, is often reduced to procedural compliance rather than being practiced as a code of conduct. The paper argues that reframing auditor responsibility requires both methodological and cultural shifts in how skepticism is exercised and communicated. Auditors must adopt more flexible risk assessment tools, actively apply skepticism, and clearly communicate their role to stakeholders. Bridging this gap is critical to maintaining public trust and the relevance of the audit profession.
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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 | 0.082 | 0.125 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.035 |
| Scholarly communication | 0.023 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.007 | 0.009 |
| 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".