“I Don't Want Someone to Watch Me While I'm Working”: Gendered Views of Facial Recognition Technology in Workplace Surveillance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Employers are increasingly using information and communication technologies to monitor employees. Such workplace surveillance is extensive in the United States, but its experience and potential consequences differ across groups based on gender. We thus sought to identify whether self‐reported male and female employees differ in the extent to which they find the use of workplace cameras equipped with facial recognition technology (FRT) acceptable, and examine the role of privacy attitudes more generally in mediating views on workplace surveillance. Using data from a nationally representative survey conducted by the Pew Research Center, we find that women are much less likely than men to approve of the use of cameras using FRT in the workplace. We then further explore whether men and women think differently about privacy, and if perceptions of privacy moderate the relationship between gender and approval of workplace surveillance. Finally, we consider the implications of these findings for privacy and surveillance via embedded technologies, and how the consequences of surveillance and technologies like FRT may be gendered. Note: We recognize evaluations based on a binary definition of gender are invariably partial and exclusionary. As we note in our discussion of the study's limitations, we were constrained by the survey categories provided by Pew.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it