HOW DO VIOLATIONS OF PRIVACY AND MORAL AUTONOMY THREATEN THE BASIS OF OUR DEMOCRACY?; pp. 369–381
Bibliographic record
Abstract
Behavior detection technologies are currently being developed to monitor and manage malintents and abnormal behavior from a distance in order to prevent terrorism and criminal attacks. We will show that serious ethical concerns are raised by capturing biometric features without informing people about the processing of their personal data. Our study of a range of European projects of second-generation biometrics, particularly of Intelligent information system supporting observation, searching and detection for security of citizens in urban environments (INDECT) and Automatic Detection of Abnormal Behaviour and Threats in crowded Spaces (ADABTS), shows that violations of privacy put several other values in jeopardy. We will argue that since privacy is in functional relationship with other values such as autonomy, liberty, equal treatment and trust, one should take this into account when limiting privacy for protecting our security. If indeed it should become necessary to restrict our privacy in specific situations, thoughtful consideration must be given to other ways of securing the values that form the foundation of our liberal democratic society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.057 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".