The Canadian Clearview AI Investigation as a Call for Digital Policy Literacy
Bibliographic record
Abstract
In 2020, the Office of the Privacy Commissioner of Canada (OPCC) led a joint federal-provincial investigation into privacy violations stemming from the use of facial recognition technologies. The investigation was prompted specifically by the mobilization of Clearview AI’s facial recognition software in law enforcement, including by regional police services as well as the Royal Canadian Mounted Police. Clearview AI’s technology is based on scraping social media images, which, as the investigation found, constitutes a privacy law violation according to provincial and federal private sector legislation. In response to the investigation, Clearview AI claimed that consent for scraping social media images was not required from users because the information is already public. This common fallacy of social media privacy serves as a pivot point for the integration of digital policy literacy into the OPCC’s digital literacy materials in order to consider the regulatory environment around digital media, alongside their political-economic and infrastructural components. Digital policy literacy is a model that expands what is typically an individual- or organization-level responsibility for privacy protection by considering the wider socio-technical context in which a company like Clearview can emerge.
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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 | 0.014 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.050 | 0.020 |
| Scholarly communication | 0.021 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".