Police use of facial recognition technology: The potential for engaging the public through co-constructed policy-making
Bibliographic record
Abstract
In the face of rapid technological development of investigative technologies, broader and more meaningful public engagement in policy-making is paramount. In this article, we identify police procurement and use of facial recognition technology (FRT) as a key example of the need for public input to avoid undermining trust in law enforcement. Specifically, public engagement should be incorporated into police decisions regarding the acquisition, use, and assessment of the effectiveness of FRT, via an oversight framework that incorporates citizen stakeholders. Genuine public engagement requires sufficient and accurate information to be openly available at the outset, and the public must be able to dialogue and discuss their perspectives and ideas with others. The approach outlined in this article could serve as a model for addressing policy development barriers that often arise in relation to privacy invasive technologies and their uses by police.
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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 | 0.064 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".