Bibliographic record
Abstract
Privacy impact assessments (PIAs) may soon be standardised. The European Commission plans to make PIAs mandatory if Article 33 of its proposed Data Protection Regulation is adopted without any serious depredations by lobbyists. Concurrently, the International Organization for Standardization (ISO) is considering a standard for PIAs. The approaches currently being pursued by the Commission and the ISO have their antecedents in the PIA methodologies used in Australia, Canada, Ireland, New Zealand, the UK and the US. However, almost no attention has been paid to actual PIA reports to see how well or poorly they have been prepared and how closely they follow the PIA guidance documents in their countries. This paper argues that it is worth doing - to review actual PIA reports to see what can be learned from how they are implemented and whether their implementation offers any signposts for the policymaking process. However, finding actual PIA reports is something of a challenge. Following a search for UK PIA reports, this paper provides the results of analysis of some of those in terms of how well they followed the ICO PIA Handbook guidance, and what we can learn from an analysis of actual PIA reports. Along the way, this paper argues that organisations in the UK should create a registry of publicly available PIA reports.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".