Tort and Data Protection Law: Are There Any Lessons to Be Learnt?
Bibliographic record
Abstract
The development and evolution of data protection law is not fully realised. One challenge that has emerged is the recognition of a tort for violating a person’s personal information contrary to data protection law. The issue is that courts have found it difficult to determine and assess the harm caused to the data subject. The courts in the United Kingdom (UK) and Canada have recently developed a tort for infringing privacy in personal data. What has emerged is that courts in those two countries have begun to establish some key principles to underpin a tort violating privacy, by providing guidance on measuring the ensuing harm. That tort is also developing in the United States. This article argues that other common law jurisdictions, notably Australia, should consider going down the same pathway, by establishing a privacy tort over the Internet. Such a tort in data protection will provide a higher level of control to data subjects over their personal data and deter entities from misusing that data. However, that tort may fail to protect data subjects from the misuse of their personal data if the law requires harm to eventuate, as is required by the tradition tort of privacy. This must be considered with caution because, unlike traditional notions of a tort in privacy, a privacy violation of over the Internet may take weeks, months or years to identify. Contrarily, tort law has been effective in reducing and deterring negligence in privacy related cases, strengthening the rationale for a tort in personal data over the Internet.
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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 | 0.026 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.061 |
| Scholarly communication | 0.018 | 0.053 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.031 | 0.035 |
| Insufficient payload (model declined to judge) | 0.013 | 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".