An analysis of auditors’ perceptions towards artificial intelligence and its contribution to audit quality
Bibliographic record
Abstract
Artificial intelligence (AI) systems have significantly changed the audit process; nevertheless, the AI revolution opponents view this growth as a step-back as many auditors will fail to adapt to this new environment and will drop behind. Our descriptive study examines the perceived ease of use, usefulness, and contribution to the audit quality of different AI’s types. To address local audit firms concerns about their readiness to use AI systems in auditing processes and to advance auditing research, we examine whether perceived ease of use, usefulness, and contribution to audit quality vary by AI systems type (Assisted, Augmented, and Autonomous). An online questionnaire was used to collect data from 124 auditors representing local audit firms in Jordan. Our results indicate that auditors perceive Assisted and Augmented AI systems as ease of use in auditing while perceiving Autonomous AI systems as complicated to use. Besides, Auditors are underestimating Autonomous AI systems’ capabilities and perceived it as not useful for auditing. The results also reveal a significant difference between the perceived contribution to audit quality by the three AI systems types. This study contributes to the existing literature on AI and auditing by developing and testing a measure for AI systems’ perceived contribution to audit quality. This study also provides empirical evidence on how Jordan local firms auditors perceive AI use in auditing.
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 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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".