Bridging the transatlantic divide? The United States, the European Union, and the protection of privacy across borders
Bibliographic record
Abstract
Revelations of mass surveillance by the US National Security Agency have produced widespread protest, notably in the EU, and have supposedly deepened the transatlantic divide between the US and the EU on matters of privacy and national security. The aim of this article is to qualify this understanding. While there are substantial differences between the US and the EU with respect to data protection from private actors, the differences are far less stark when it comes to restrictions on state surveillance for national security purposes. In particular, in both regimes privacy protections apply mainly territorially, to the benefit of citizen residents, while few if any legal limits constrain the capacity of intelligence agencies to conduct surveillance of non-citizens outside their borders. As a result, EU citizens are vulnerable to US surveillance, and US citizens are vulnerable to surveillance by European states. In the absence of transformation of domestic law, we maintain that a transatlantic agreement is necessary if privacy is to be safeguarded effectively. We identify several strong legal and policy arguments why the EU and the US should adopt a transnational compact restricting the powers of their own intelligence agencies to spy on each other’s citizens. While there are undoubtedly concerns about what the content of such an agreement might look like, any degree of transnational protection would be an improvement over the current state of affairs. The capacity of nations to engage in dragnet surveillance has gone global, and unless law catches up, privacy rights will be left behind.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".