CHALLENGES OF AI FOR RESEARCH ETHICS
Bibliographic record
Abstract
With the continuously increasing popularity of AI, it is critical to gain an understanding of how research ethics are impacted. Unfortunately, the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans, or the TCPS2, lags far behind current technology and potential legislation when it comes to AI. We suspect that REBs at Canadian public universities are struggling to determine how the TCPS2 can or should be interpreted when it comes to AI research. To gauge these challenges, we conducted a survey of REB Chairs/Vice-Chairs and REO managers/supervisors. The results of the survey confirmed our suspicions. They also show that the lack of guidance from the Tri-Council and the Panel on Research Ethics is likely making this struggle worse. Through an analysis of the TCPS2, proposed legislation, and the survey results, we developed a series of recommendations for both the Tri-Council and REBs which may help overcome these struggles and ease the confusion.
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 | 0.472 | 0.482 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.034 | 0.119 |
| Scholarly communication | 0.050 | 0.037 |
| Open science | 0.006 | 0.021 |
| Research integrity | 0.022 | 0.048 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".