Position Statement on Artificial Intelligence (AI) Use in Evidence Synthesis Across Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence 2025
Bibliographic record
Abstract
Evidence synthesists are ultimately responsible for their evidence synthesis, including the decision to use artificial intelligence (AI) and automation, and to ensure adherence to legal and ethical standards.Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence support the aims of the Responsible use of AI in evidence SynthEsis (RAISE) recommendations, which provide a framework for ensuring responsible use of AI and automation across all roles within the evidence synthesis ecosystem.Evidence synthesists developing and publishing syntheses with Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence can use AI and automation as long as they can demonstrate that it will not compromise the methodological rigor or integrity of their synthesis.AI and automation in evidence synthesis should be used with human oversight.Any use of AI or automation that makes or suggests judgements should be fully and transparently reported in the evidence synthesis report.AI tool developers should proactively ensure their AI systems or tools adhere to the RAISE recommendations so we have clear, transparent, and publicly available information to inform decisions about whether an AI system or tool could and should be used in evidence synthesis.
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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 | 0.603 | 0.796 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.014 | 0.021 |
| Bibliometrics | 0.030 | 0.031 |
| Science and technology studies | 0.012 | 0.020 |
| Scholarly communication | 0.055 | 0.024 |
| Open science | 0.024 | 0.030 |
| Research integrity | 0.101 | 0.079 |
| Insufficient payload (model declined to judge) | 0.024 | 0.026 |
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".