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Record W4413400919 · doi:10.54097/nq93v031

Auditor Responsibility in the Context of Forward-Looking Judgments, Fraud Risk, and Evolving Stakeholder Expectations

2025· article· en· W4413400919 on OpenAlexaff

Bibliographic record

VenueHighlights in Business Economics and Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBusinessStakeholderContext (archaeology)AuditAccountingPublic relationsPolitical scienceHistory

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.210
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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