Distinguishing Friend from Foe: Law and Policy in the Age of Battlefield Biometrics
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
Summary In the space of just over ten years, the collection and use of biometric data in the context of international military operations has gone from being virtually unheard of to being an everyday occurrence. The Canadian and US armed forces operating in Afghanistan, for example, have together collected the digital fingerprints, eye scans, and digital photographs of more than 2.5 million Afghans. The introduction of biometrics technology to warfare has undoubtedly increased the security of the armed forces that use it and made it easier for them to kill or capture their enemies. Its effectiveness, reliability, and convenience have all been praised. Due in part to its novelty, however, law and policy relating to the use of biometrics in conflict situations remain underdeveloped. This underdevelopment poses considerable risks for the already vulnerable populations who are being subjected to these programs, potentially including violations of their right to privacy, misuse of their personal data, or their misidentification as enemies or threats. This article weighs these benefits and risks associated with biometrics technology. It analyzes the extent to which law and policy already govern the collection and use of biometrics by armed forces at both the domestic and international levels. It explores why the United States and Canada — the two states whose armed forces appear to be the most heavily engaged in the collection of biometric data abroad — have adopted such different policies with respect to the use of biometrics. It explains why the current international legal and policy vacuum in relation to battlefield biometrics is unacceptable and concludes that the time to discuss best practices is now. Ten non-legally binding guidelines are proposed for consideration and potential adoption by states.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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