The Influence of Audit Firm Culture on the Adoption of Artificial Intelligence in Audit Firms
Bibliographic record
Abstract
Today, automation, digitization, and integration of AI are becoming increasingly pervasive in the work environment, and as AI applications become integral to the rapid evolution of digital workplaces, a critical audit concern revolves around how professionals respond to this new milieu; in this realm, the adoption of AI in professional practice may require a cultural shift given that auditors tend to be cautious individuals (Davidson & Dalby, 1993). As a cutting-edge information technology, AI can offer several advantages in auditing, including the ability to process large amounts of data, such as bank statements and legal contracts, much faster than human auditors, leading to more efficient reconciliation of accounts and improved audit quality. AI also minimizes over testing, allowing auditors to perform more efficient and targeted audit procedures (Dennis, 2024). However, there are significant challenges to analyzing such data and producing up-to-date audit documentation, such as AI's inability to perform actions or controls independently and its lack of ability to make moral or ethical judgments (CPA Canada & AICPA, 2020). The challenges posed by AI, coupled with the inherently risk-averse nature of auditors, particularly audit partners facing potential litigation risks, highlight the significant influence of tone at the top in shaping audit firm culture. This risk aversion may contribute to auditors' reluctance to adopt a new and relatively unproven technology such as AI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".