AI and the Accounting Profession: Views from Industry and Academia
Bibliographic record
Abstract
ABSTRACT Anecdotal and empirical evidence indicates that the growing adoption of artificial intelligence (AI) within accounting firms and accounting departments leads to improvements in efficiency, a gradual increase in the share of AI workers, and a decrease in junior accounting employees. If this trend continues, would it signal the beginning of an era of diminishing demand for new accounting professionals and a shift in the required skill set of new accounting employees? The aim of the workshop, which, by happenstance, occurred the same week that OpenAI introduced ChatGPT, was to bring together Accounting Information Systems researchers and representatives from leading accounting firms for a conversation on the implications of AI for the accounting profession and related research opportunities. Although the panelists at the time had no way of knowing the capabilities of generative AI models like ChatGPT, their main message was timely and appropriate: Accountants with AI will replace accountants.
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 | 0.025 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.030 |
| Scholarly communication | 0.027 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".