AI Governance in Academia: Guidelines for Generative AI
Bibliographic record
Abstract
Generative AI tools tend to be used as if they were built to gather or confirm truthful information, as if they were knowledge-based systems. As such, there is a discrepancy between how generative AI (e.g. ChatGPT) is conceived and used by the general public, and what it really is and can accomplish. Given a lack of proper legal framework and the widespread usage of these tools, organizations have raised red flags and urged academic institutions to reflect on governance principles for the use of generative AI. In this paper, we present the principles adopted by an Institutional AI committee to guide usage of generative AI, as well as the theoretical and practical considerations motivating their introduction.
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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 | 0.165 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.012 | 0.056 |
| Scholarly communication | 0.030 | 0.022 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.032 | 0.030 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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".