Artificial Intelligence in Governance: The State of Facial Recognition Technology in Canada
Bibliographic record
Abstract
Innovation in public service delivery can help the rapid transformation of society into a post-COVID era. In addition to minimizing administrative hassles, efficiently using Artificial Intelligence (AI) can protect citizens from unwanted behaviors. AI broadly denotes the efficiency of computers in replicating human intelligence, such as identifying different patterns and making predictions and decisions. AI encompasses numerous techniques, and machine learning is one of the most widely used. Machine learning is a method of deploying large datasets to make predictions that improve over time with more data. By 2030, Canada aims to have one of the most robust national AI ecosystems in the world, founded upon scientific excellence, high-quality training, deep talent pools, public-private collaboration, and their strong value of advancing AI technologies to bring positive social, economic, and environmental benefits for people and the planet. This study intended to assess the overall situation of AI in governance and policy compliance. I found that the country relies on patchwork and faces numerous legal and practical issues owing to the absence of an umbrella policy and organization. This research also proposes ideas to enhance governance to improve biometric data protection, legal frameworks, and quality standards for collecting biometric data based on the FRT. This study is based on focus group discussions, policy papers of the government of Canada, and many other literature and research articles.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".