AI ethics in a research context: current approaches and future challenges
Bibliographic record
Abstract
The rise of artificial intelligence (AI) as a research tool has significant implications for research ethics boards. Some institutions may transfer broad AI review responsibilities on to REBs, while other REBs may consider AI tools only within specific proposals for research involving human participants. Both scenarios will present challenging questions requiring REB members to apply core understandings of how AI works and how to apply principles of ethical AI in the field of the research project. Facilitated by some of Canada’s leading experts in AI ethics, this workshop will provide participants with a solid grounding in the nature of AI tools and advances in AI ethics as well as examples of AI ethics challenges to date. Participants will then leverage this information in smaller-group discussions on how AI principles can apply to REB assessments in specific research disciplines (e.g. medicine, social science, etc.). Topics of discussion will include: choosing amongst the various existing ethical AI frameworks (e.g. the Montreal Declaration in Canada, the Berkman-Klein Principled AI Framework in the USA, and the Digital Catapult Ethical Framework in the UK), reconciling inconsistencies between different frameworks, addressing the underlying tension between individual ethical principles (e.g. participant privacy versus data transparency), and how AI-specific guidance is situated within the broader landscape of research ethics and existing regulatory frameworks.
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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 | 0.287 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.023 | 0.104 |
| Scholarly communication | 0.061 | 0.064 |
| Open science | 0.012 | 0.029 |
| Research integrity | 0.035 | 0.044 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".