Automating the Thin Blue Line: Controversy, Knowledge, and the Governance of Police Technology in Canada
Bibliographic record
Abstract
In 2020, journalists uncovered that Canadian police services had surreptitiously acquired, used, and lied about Clearview AI, an invasive facial recognition technology. The revelations sparked controversy, revealing gaps in how police technology is regulated in Canada. This project uses the Clearview AI controversy to explore the social, political, legal, and cultural challenges that police technology poses for Canadian governance regimes. Drawing upon scholarship from policing studies, governance studies, and science and technology studies, this project argues that attempts at governing police technology in Canada fail due to police secrecy, ambiguities at the intersection of the public and private sector, and a patchwork of bodies and institutions with incomplete regulatory authority over both policing and technology. Automating the Thin Blue Line explores these issues through ethnographic observations, content analysis of nearly a hundred government documents, and over 50 interviews with stakeholders from policing, government, civil society, and private industry. This project shows how the Clearview AI controversy revealed previously unexplored gaps in how police buy, use, and control cutting-edge technology. It also demonstrates how controversies shape technological governance, arguing that police departments across Canada responded to this controversy by mounting seemingly collaborative governance initiatives with advocacy groups, governmental bodies, and the Canadian public. My project finds that these initiatives failed to materialize because of asymmetrical power relations that favour police and private industry, resulting in governance mechanisms that are collaborative in name alone. I also engage reflexively with my own challenges accessing research participants from police departments, demonstrating how governance initiatives are often framed as attempting to dismantle—but ultimately cannot overcome—the culture of secrecy and inaccessibility that pervades policing. These issues put police’s regulatory identity in crisis: police wind up caught between the rule-bound bureaucracy of the public sector and the move-fast-and-break-things ethos of private technology companies. Overall, this project uses empirical exploration to showcase the growing range of relevant actors involved in police technology’s governance. It uses this vantage point to examine not only top-down dynamics of cooperation, knowledge governance, and controversy closure between these relevant actors, but also the friction, disagreement, and distrust that characterizes their relationships.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".