Public perceptions of facial recognition use by police in Canada
Bibliographic record
Abstract
This study investigates public perceptions of facial recognition technology (FRT) employed by police to understand its implications for police-community relations. Despite the potential advantages of FRT in identifying suspects and vulnerable populations, research on its impact on public trust and police legitimacy is limited. Our analysis incorporates the results from a survey conducted with a representative sample from Toronto and surrounding areas, in Ontario, Canada, exploring comfort levels regarding various police uses of FRT. Findings reveal that public comfort varied depending on the context of FRT application; respondents largely approved of FRT for serious incidents or specific suspect identification, while also expressing discomfort with its use for minor incidents and/or more diffuse surveillance. Notably, comfort was higher when FRT applications demonstrated practical value, such as identifying missing persons. Secondly, positive attitudes toward the police were significantly linked to greater comfort with FRT usage. This research underscores the necessity of considering public perceptions as policing technologies and the policies that govern them evolve. As police services increasingly integrate FRT, understanding community attitudes becomes crucial for fostering trust and legitimacy in policing practices. Future research should further explore the nuances of public sentiment regarding technological innovations in policing, ensuring that community voices are integral to decision-making processes surrounding technological adoption and use.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".