Improving privacy protection in the area of behavioural targeting
Bibliographic record
Abstract
Behavioural targeting, or online profiling, is at the core of many privacy problems on the Internet. Behavioural targeting involves monitoring people’s online behaviour and using the data obtained to expose people to individually targeted advertisements. In the process, firms gather information, store it, analyse it, and disclose it to other firms. Firms compile detailed profiles, based on what Internet users read, what videos they watch, what they search for, etc. People have litlle control over what happens to information concerning them. There is wide agreement that EU data protection law - and similar regimes in countries worldwide - offers insufficient protection of privacy on the Internet. This publication examines how the law could improve online privacy protection, and is among the first legal studies to discuss the implications of behavioural sciences for privacy law. A detailed analysis is presented of the problematic role of informed consent in data protection law, emphasising the tension in the law between protecting and empowering the individual. [...] Given the limited potential of informed consent as a privacy protection measure, the publication argues that policymakers can improve legal privacy protection by focusing less on empowering people and more on protecting people. Practitioners, businesspersons, policymakers, and regulators will find much here to help them develop a more cogent, socially responsible, and reasonable approach to privacy law and policy - not only in Europe but anywhere in the world."
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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 | 0.048 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.013 | 0.019 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.014 | 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".