Comparative Law Research on the Personal Data Protection Law in Various Countries
Bibliographic record
Abstract
In the JST Moonshot R&D Project (Goal 9), "Legal Principles of Decentralized Management" (proposed by Tatsuhiko Yamamoto, professor at Keio University), we discuss the benefits and challenges that arise from social implementation of personal AI from a legal perspective. It is analyzed from Personal AI is AI that manages personal data on behalf of the individual based on the individual's privacy preferences. This can be seen as a tool to back up the right to information self-determination (the right to control one's own information). This research is a comparative study of personal information protection legislation in the EU, Germany, France, Switzerland, the United States, Canada, South Korea, Taiwan, Thailand, and China. We asked report authors from each country to investigate how mechanisms for individual involvement (right to request deletion, right to access, consent, right to data portability) are stipulated in personal information protection laws. We examined the significance and challenges of the right to information self-determination, paying particular attention to the relationship between the Constitution and the Personal Information Protection Act.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".