Identity and War: The Role of Biometrics in the Russia-Ukraine Crisis
Bibliographic record
Abstract
On February 24, 2022, Russia launched a full-scale invasion of Ukraine. While many experts have examined the conflict from geopolitical, economic, and humanitarian angles, few have formally studied biometric technologies' role in the conflict. Our analytical survey helps fill this gap. On one hand, the faces and fingerprints of refugees are being used to protect the national security of asylum countries in what has become the worst refugee crisis since WWII. Biometric passports simplify travel for Ukrainian refugees to countries in the European Union through a visa-free traveling regime. Meanwhile, the United Nations (UN) uses fingerprinting to distribute cash aid to eligible Ukrainian refugees securely. Biometrics may also become a potent weapon for fighting the war-exacerbated human trafficking crisis in Ukraine. On the other hand, refugees applying for Canadian, UK, and other visas were subject to long waiting times to fulfill the biometric registration requirement of the visa application. Biometrics were also forcefully collected from Ukrainians deported to Russia from Russian-occupied territories in Ukraine. Meanwhile, Russia uses public face recognition to identify, arrest, and prosecute anti-war activists. From the refugee crisis to the battlefield to information warfare, our work analyzes reports of how the use of biometric technologies has impacted the ongoing conflict. We also present potential solutions to problems stemming from the use of biometrics during the ongoing conflict. Our examination of the conflict through a lens of biometrics applications can help researchers and analysts deepen their comprehension of the ongoing war as well as other and future conflicts. The information presented here is current as of the time of the writing. The reader interested in the subject presented here is highly encouraged to follow the latest reports and analyses from the sources tracking the conflict
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".