Statewide Adoption of the AICPA Code of Professional Conduct: A Review of Recent AICPA Disciplinary Actions
Bibliographic record
Abstract
This study reviews the AICPA disciplinary process and examines a recent sample of disciplinary actions taken against practitioners for ethics violations. Trends related to enforcement and disclosure of actions are inspected and additional details are provided based on state codes of conduct. We consider the effects of uniform statewide adoption of the AICPA Code of Professional Conduct for CPAs, as recently encouraged by the AICPA and NASBA. We find 43% of state accounting boards have formally adopted the AICPA Code of Professional Conduct, 35% have not adopted the Code and 22% have partially adopted the Code. The three states with the highest number of disciplinary actions are New York, California and Texas, none of which have adopted the Code. Of the top ten states with the greatest number of enforcement actions, only two have formally adopted the Code. The most common type of investigation in the sample is an automatic disciplinary provision by the AICPA. Dispositions for violations appear to be getting more severe, with admonishments declining and settlements, terminations and suspensions taking its place.
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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 | 0.016 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".