Algorithmic Policing Technologies in Canada
Bibliographic record
Abstract
Canadian law enforcement agencies are applying algorithmic technologies to identify individuals at the regional, provincial, and federal levels. These technologies connect templated facial images to an array of informational fragments that are collected from databases scattered between the public and private sectors. While that is the case, these surveillance technologies continue to be authorized under SCC jurisprudence, as opposed to legislation enacted by Parliament. Algorithmic technologies collate and analyze disparate information from public and private databases to identify patterns, which are then used to generate formulas to ‘predict’ future trends. Kate Robertson and colleagues explain that implementation of APTs by Canadian police services holds serious deleterious potential for the Charter rights of Canadians, with consequences that disproportionately affect people of colour. Be that as it may, the most malevolent consequence of applying APTs may be their application of generalized formulas to generate recommendations used to intercept individuals based on biased and inaccurate information. Although not authorized by statute, surveillance technologies continue to be permissible under common law authorities. Richard Jochelson explains the inappropriate nature of this approach, arguing in the alternative that the court’s traditional role calls for application of the Oakes test to determine if state surveillant practices fall within its constitutional limits. Considering APT’s serious implications for Charter protected rights, this paper calls on legislators to implement dedicated legislation to govern the use of surveillant technologies in law enforcement, with a particular focus on regulating the use of APTs. Failure to do so risks an unprecedented expansion of prejudicial policing practices, which may act to crystallize the existing biases in law enforcement practices into objective ‘scientific’ outputs that may hold serious deleterious potential for Canada’s most vulnerable populations.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".