Bytes and battles: Inclusion of data governance in responsible military AI
Bibliographic record
Abstract
Data plays a critical role in the training, testing and use of artificial intelligence (AI), including in the military domain. Research and development for AI-enabled military solutions is proceeding at a rapid pace; however, pathways and governance solutions to address concerns (such as issues with the availability and quality of training data sets) are lacking. This paper provides a comprehensive overview of data issues surrounding the development, deployment and use of AI; examines data governance lessons and practices from civilian applications; and identifies pathways through which data governance could be enacted. The paper concludes with an overview of possible policy and governance approaches to data practices surrounding military AI to foster the responsible development, testing, deployment and use of AI in the military domain.
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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 | 0.048 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.028 | 0.042 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".