Lawful Illegality: Authorizing Extraterritorial Police Surveillance
Bibliographic record
Abstract
This paper examines Lisa Austin’s (2015) concept of lawful illegality, which interrogates the legal foundations for potentially unlawful surveillance practices by United States (US) signals intelligence (SIGINT) agencies. Lawful illegality involves the technically lawful operation of surveillance powers that might be considered unlawful when examined through a rule of law framework. We argue lawful illegality is expanding into domestic policing through judicial decisions that sanction complex and technically sophisticated forms of remote online surveillance, such as the use of malware, remote hacking, or Network Investigative Techniques (NITs). Operation Pacifier targeted and dismantled the Playpen dark web site, which was used for distributing child exploitation material (CEM), and has generated many judicial rulings examining the legality of remote surveillance by the FBI. We have selected two contrasting cases that demonstrate how US domestic courts have employed distinct logics to determine the admissibility of evidence collected through the NIT deployed in Operation Pacifier. The first case, United States v. Carlson (2017 US Dist. LEXIS 67991), offers a critical view of the use of NITs by the FBI, with physical geography constraining the legality of this form of surveillance in US criminal procedure. The second case, United States v. Gaver (2017 US Dist. LEXIS 44757), authorizes the use of NITs because the need to control crime is believed to justify suspending the geographic limits on police surveillance to identify people involved in the creation and dissemination of CEM. We argue this crime control emphasis expands the reach of US police surveillance while undermining due process of law by removing the protective function of geography. We conclude by suggesting the permissive geographic scope of police surveillance reflected in United States v. Gaver (2017 US Dist. LEXIS 44757), and many other Playpen cases, erodes due process for all crime suspects, but is particularly acute for people located outside the US, and suggest a neutral transnational arbiter could help limit contentious forms of remote extraterritorial police surveillance.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".