The Fourth Amendment Limits of Facial Recognition at the Border
Bibliographic record
Abstract
On any given day, hundreds of thousands of people enter the United States through ports of entry along the Mexican and Canadian borders. At the same time, the Department of Homeland Security (“DHS”) seizes millions of dollars’ worth of contraband entering the United States annually. Under the border-search exception, border officials can perform routine, warrantless searches for this contraband, based on no suspicion of a crime, without violating the Fourth Amendment. But as DHS integrates modern technology into its enforcement efforts, the question becomes how these tools fit into the border-search doctrine. Facial recognition technology (“FRT”) is a prime example. To date, no court—and few legal scholars—have addressed how the Fourth Amendment would regulate the use of FRT at the border. This Note begins to fill that gap. This Note contends that, after Carpenter v. United States, the Fourth Amendment places at least some limits on the use of FRT at the border. Given the absence of caselaw, this Note uses a hypothetical border search to make three core claims. First—distinguishing between face verification and face identification—this Note argues that face identification constitutes a Fourth Amendment “search” only when the images displayed to a border official reveal “the privacies of life.” Second, because of its invasive nature, this form of face identification is a nonroutine border search and is unconstitutional when conducted without reasonable suspicion. Lastly, this Note concludes that a border official’s reasonable suspicion must be linked to a crime that bears some nexus to the purposes underlying the border-search exception.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.016 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.023 | 0.017 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".