Facial recognition technology for policing and surveillance in the Global South: a call for bans
Bibliographic record
Abstract
The use of facial recognition technology (FRT) for policing and surveillance is spreading across Asia, Africa and Latin America. Advocates are saying this technology can solve crimes, locate missing people and prevent terrorist attacks. Yet, as this article argues, deploying FRT for policing and surveillance poses a grave threat to civil society, especially systems to identify or track people without any criminal history. In every political system, this has the potential to deepen discriminatory policing, have a chilling effect on activism and turn everyone into a suspect. The dangers rise exponentially, moreover, in places with inconsistent rule of law, poor human rights records, weak privacy and data laws and authoritarian rulers – traits common across scores of countries now installing FRT. Regulating use is unlikely to prevent these harms, the article contends, given the powerful political and corporate forces in play, given the ways firms push legal limits, exploit loopholes and lobby legislators, and given the tendency over time of surveillance technology to creep across state agencies and into new forms of social control. Calls to ban FRT are growing louder by the day. This article makes the case for why bans are especially necessary in the Global South.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".