Biomedical data repositories require governance for artificial intelligence/machine learning applications at every step
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objectives: The NIH's Bridge2AI Program has funded 4 "new flagship biomedical and behavioral datasets that are properly documented and ready for use with AI [artificial intelligence] or ML [machine learning] technologies" to promote the adoption of AI. This article discusses the challenges and lessons learned in data collection and governance to ensure their responsible use. Materials and Methods: We outline major steps involved in creating and using these datasets in ethically acceptable ways, including (1) data selection-what data are being selected and why, (2) increasing attention to public concerns, (3) the role of participant consent depending on data source, (4) ensuring responsible use, (5) where and how data are stored, (6) what control participants have over data sharing, (7) data access, and (8) data download. Results: We discuss ethical, legal, social, and practical challenges raised at each step of creating AI-ready datasets, noting the importance of addressing issues of future data storage and use. We identify some of the many choices that these projects have made, including how to incorporate public input, where to store data, and defining criteria for access to and downloading data. Discussion: The processes involved in the establishment and governance of the Bridge2AI datasets vary widely but have common elements, suggesting opportunities for future programs to lean upon Bridge2AI strategies. Conclusions: This article discusses the challenges and lessons learned in data collection and governance to ensure their responsible use, particularly as confronted by the 4 distinct projects funded by this program.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it