Amazon vs. My Brother: How Users of Shared Smart Speakers Perceive and Cope with Privacy Risks
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
With the rapid adoption of smart speakers in people's homes, there is a corresponding increase in users' privacy and security concerns. In contrast to previous studies of users' concerns about smart speakers' divulging private information to their manufacturers, our study focused on investigating users' concerns with regard to housemates and external entities. We conducted semi-structured interviews with 26 participants living in 21 households. Our results suggest that users often have an inadequate understanding of what data their smart speakers makes available to all users and what is kept private. Although participants expressed different privacy concerns about their housemates and external entities, they adopted similar, yet suboptimal, risk management strategies. We provide recommendations for future speaker design to support more optimal coping with the perceived risks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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