Digital Security Governance and Accountability in Europe: Ethical Dilemmas in Terrorism Risk Management
Bibliographic record
Abstract
Digital security governance – the use of digital personal data for threat analysis on the basis of (automated) risk profiling – enhances terrorism risk management in Europe. European security strategies emphasise that information and communication technology increasingly play a key role in preventing and anticipating threats such as terrorism and cyber-crime. It enables, for example, the sharing of personal, financial or travellers’ data with third countries. This article focuses on digital security governance in the context of the Passenger Name Record (PNR), the Advance Passenger Information (API) and the Terrorist Finance Tracking System (TFTP) programmes. Particularly, it considers the ethical dilemmas of using and sharing digital personal data as well as accountability for this type of risk management. Because there are broader socio-political, legal and technological issues connected to the use of information and communication technology for digital security governance, the concept of accountability in this article is holistic.
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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 | 0.049 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.042 |
| Scholarly communication | 0.026 | 0.016 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".