Debate Introduction: The Privacy and Surveillance Implications of Police Body Cameras
Bibliographic record
Abstract
Body-worn cameras are being acquired and deployed by police departments around the world at an increasingly rapid pace.Like many technologies before them, body-worn cameras promise to change the way police work is done-and, indeed, this is a primary claim made by civil liberties and other advocacy groups who are promoting their adoption as a means of "civilizing" the police and increasing police accountability (e.g., Stanley 2015).However, for many, the use of police body-worn cameras also raises interesting and vital privacy and surveillance-related concerns.Like other forms of surveillance, bodyworn cameras gather information and preserve it in a form amendable to processing and analysis but, unlike traditional CCTV or other static cameras, they can also make their way into private homes-and anywhere else police choose to go-and record everything they see and hear.These body cameras often come with a rarely discussed range of surveillance capacities that, precisely because they are mobile, raise unique privacy and other issues.Since 2011, the debate section of Surveillance & Society has encouraged the exchange of new ideas and the discussion of important concepts and trends by selected expert participants.These contributions appear together to foment scholarship and commentary in this journal and beyond.Discussants to this debate, as in prior years, were invited to contribute short papers on a selected topic; in this instance, the privacy and surveillance implications of police-worn body cameras.The six debate contributions in this issue from four countries address the following question: Are the surveillance and privacy implications of widespread adoption of body-worn cameras by public police worth the alleged increase in accountability?Why ask about body-worn cameras?One reason is that, like many other surveillance technologies, they promise greater accountability and, in this respect, are exemplary of such technologies.Body-worn cameras also represent the new mobility of video surveillance, which, until recently, has tended to be stationary, but is now propelled by humans, drones, automobiles, and other vehicles, raising a host of new issues.While body-worn cameras have been piloted and slowly implemented in public police organizations in Europe, the United States, Canada, Australia, and elsewhere for years, it was the events of Ferguson, Missouri, in 2014 that led to greater public and political consideration of these devices as a solution to inadequate police accountability, raising the visibility of body camera adoption around the world.Even U.S. President Barack Obama expressed support, and promised funding, for rolling out these cameras in police departments across the U.S.
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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 | 0.018 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.043 | 0.025 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".