CompAi: A Tool for GDPR Completeness Checking of Privacy Policies using Artificial Intelligence
Bibliographic record
Abstract
We introduce CompAı - a tool for checking the completeness of privacy policies against the general data protection regulation (GDPR). CompAı facilitates the analysis of privacy policies to check their compliance to GDPR requirements. Since privacy policies serve as an agreement between a software system and its prospective users, the policy must fully capture such requirements to ensure that collected personal data of individuals (or users) remains protected as specified by the GDPR. For a given privacy policy, CompAı semantically analyzes its textual content against a comprehensive conceptual model which captures all information types that might appear in any policy. Based on this analysis, alongside some input from the end user, CompAı can determine the potential incompleteness violations in the input policy with an accuracy of ≈96%. CompAı generates a detailed report that can be easily reviewed and validated by experts. The source code of CompAı is publicly available on https://figshare.com/articles/online_resource/CompAI/23676069, and a demo of the tool is available on https://youtu.be/zwa_tM3fXHU.
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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 | 0.016 | 0.111 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".