Public perceptions of facial recognition use by police in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it