‘Are we good? or do we need to keep going?’: unraveling auditors’ comfort with evidence sufficiency determinations
Bibliographic record
Abstract
Determining when sufficient appropriate evidence has been gathered is a critical aspect of audit judgment, with regulators citing insufficient evidence as a key deficiency in audits. Drawing on interviews with 45 auditors across firms of varying sizes and using a theoretical framework that integrates the structured and affective dimensions of professional judgment, our study explores how auditors approach evidence sufficiency determinations. While auditors begin with established guidelines such as predefined document lists or materiality thresholds, these often prove insufficient or ill-suited to specific scenarios, triggering discomfort. This discomfort may prompt auditors to seek relief by adjusting evidence expectations through revised thresholds, incremental evidence gathering, and the construction of mental models. Auditors may also incorporate experiential factors, renewing their sense of what counts as ‘enough,’ or draw on relational cues such as client interactions and inputs from reviewers and team members to support comfort renewal and/or achieve relief. Comfort reflects a provisional and dynamic state emerging through a recursive feedback loop between structured procedures, interpersonal interactions, and auditors’ affective sense of when evidence sufficiency has been achieved. These findings offer a more nuanced understanding of how evidence sufficiency judgments unfold in practice, with implications for audit research and standard setting.
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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 | 0.214 | 0.342 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 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".