Submission to the Office of the Privacy Commissioner of Canada: Consultation on Consent and Privacy
Bibliographic record
Abstract
The current consent model is inadequate to protect the legitimate privacy interests of individuals in a time of increased technological complexity. Since many of the historical conditions and assumptions underlying the adoption of the current consent model have become outdated, this submission argues that measures to strengthen consent need to be taken to ensure that it is meaningful.\nThe submission rejects the argument that consent requirements should be relaxed, as this would be detrimental to the fundamental privacy rights of individuals and it would fail to achieve the goals of PIPEDA. The PIPEDA framework is based on a set of balancing principles, and a purposive approach should be taken in re-calibrated these principles from time to time. Instead of relaxing consent requirements, the consent model needs to be strengthened and supplemented with other regulatory measures.\nWe propose to enhance informed consent by making privacy policies/terms of service more understandable and giving users and consumers better ways to understand and express their privacy preferences. We recognize a troubling paradox of consent (if the information provided in the privacy policy is shorter, a person may not be fully informed, but if the full information is provided it can become too long to reasonably expect a person to fully read and understand it) and the consequent need to craft more accessible and understandable privacy policies/terms of service. Toward this end we propose that the OPC undertake to develop a model privacy policy/terms of service.\nBut while improving informed consent is a necessary step towards achieving the overall policy goals of protecting privacy, it is by no means a complete solution, so we also discuss further accountability and regulatory measures that will supplement making privacy policies more understandable.\nThe submission argues that consumers should not be penalized for expressing their privacy preferences in a way that withholds consent.\nWe also propose that data generated from Internet of Things (IoT) applications should be presumed to be sensitive and also that IoT generated data be deemed to be “personal information” even if it has been allegedly depersonalized. This is due to the highly increased risk of repersonalization, and the ability of powerful algorithms to make sensitive inferences from otherwise insensitive information.\nWe will conclude with a proposal for several textual revisions to PIPEDA Principle 4.
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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 | 0.053 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.023 | 0.013 |
| Scholarly communication | 0.023 | 0.006 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.077 | 0.035 |
| Insufficient payload (model declined to judge) | 0.036 | 0.019 |
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".