FEATURES OF IMPLEMENTATION OF THE RIGHT OF AN INDIVIDUAL TO DESTROY INFORMATION REGARDING HIMSELF (RIGHT TO BE FORGOTTEN)
Bibliographic record
Abstract
The article defines the content of the concept of an individual’s right to destroy information regarding himself (the right to be forgotten) as one of the most important elements of the right of an individual to information regarding himself, which consists in the ability of an individual to demand deletion or erasure of personal information. The author determines that an individual’s right to destroy of information regarding himself enables an individual to control the availability and dissemination of information regarding himself, which may prevent the endless dissemination of personal information about an individual and potential damage to him or his confidentiality in the future. The author determines that the EU has made a significant contribution to the regulation of the right to be forgotten in the complex of personal data protection legislation, since EU Regulation 2016/679 sets out the conditions for acquiring such a right, the grounds for unconditional fulfillment of a request for deletion of information regarding oneself and the cases in which this right may be limited. However, the latter does not consider the entire scope of personal information, which complicates the mechanism for exercising this right. The analysis also revealed that the United States does not have a unified federal legislative regulation of the right to erase or destroy, and that court practice generally does not satisfy claims for the erasure of information regarding oneself, referring to the provisions of the First Amendment to the Constitution. In Canada, civil law is focused on certain cases of exercising the right to demand the removal or correction of certain information by the original source, but the Canadian legal framework does not define the right to be forgotten. The concept of the civil right to be forgotten in the understanding of the legal system in Ukraine is also not defined. Summing up, the author notes that the case law of the European Court of Human Rights is currently focused on establishing the criteria and limits of balancing between Article 8 of the European Convention on Human Rights (ECHR) and the data controller’s right to freedom of expression under Article 10 of the ECHR in terms of ensuring the right of an individual to be forgotten. In Ukraine also there is an urgent need to regulate the right of an individual to information regarding himself, especially in terms of his right to erase such information and to regulate the mechanism for exercising the right of an individual to destroy information regarding himself.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".