The impact of artificial intelligence on tax compliance through the mediating role of electronic auditing
Bibliographic record
Abstract
This study aimed to demonstrate the impact of artificial intelligence with its dimensions (genetic algorithms, neural networks, intelligent agents, and machine learning) on tax compliance through the mediating role of electronic auditing. The research method used in this study was the descriptive analytic technique, to describe and analyze the phenomenon of the study, which is the effect of artificial intelligence on tax compliance through the mediation of electronic auditing and its relevance to the Jordanian tax authorities. The targeted study population was 205 auditors comprising the directorate of first commercial, directorate of second commercial, directorate of industrial and directorate of services of the income and sales tax department. These are the persons who represent the parties with special knowledge and experience in tax auditing issues. Because of the restricted number of people in the research, an extensive survey technique was applied to choose the study sample. The sample for this research study became the population amounting to 205 people in the Director of Large Taxpayer and medium Tax Payer Department of Income and Sales Tax. SPSS V. 20 was used to process and analyze all the data employing several statistical techniques. In line with the stated objectives, the study established important conclusions where the first was the presence of a significant effect of artificial intelligence on tax compliance through the influential electronic auditing variable. This means to emphasize the significance and function of technologies and AI in the sphere of electronic auditing and to develop their potential in organizing the tax processes and increasing the level of tax compliance. The first systematic suggestions stress the fact that improvements and modernization of the IT environment within the scope of tax departments is needed in order to ensure sufficient support for electronic auditing and artificial intelligence, and to equip the latter with suitable tools and big data analysis software.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".