User Perception of Smart Home Surveillance: An Integrative Review
Bibliographic record
Abstract
Smart Home Technologies (SHTs) have recently become popular for a variety of purposes, including healthcare, entertainment, and convenience, among others. While SHT manufacturers promise to provide a range of services relating to home security, health and wellness, automated domestic tasks, entertainment, and beyond, user perceptions vary widely in terms of benefits and drawbacks. Moreover, surveillance studies researchers have warned against normalizing technologies that may exacerbate uneven power dynamics between users and household members, marketing companies, insurance brokers, law enforcement, and others. Through an analysis of the interdisciplinary literature stemming from computer science and engineering, gerontology, the social sciences, and related fields, we explore the extent to which these potential risks and related concerns are reflected upon by smart home users. This scoping review aims to explore SHT user perceptions of privacy attitudes, the purposes of smart home surveillance, risks and benefits, and impacts on home safety. Through our review of sixty-eight relevant studies, we found that many smart home users reported satisfaction over perceived benefits such as an increased sense of safety and home security. Many others displayed limited understandings of data collection practices or expressed privacy concerns. Nonetheless, SHT usage prevailed among these users. Others report a perceived trade-off between privacy and other factors, such as convenience, and some may have resorted to privacy cynicism, a coping mechanism for dealing with ubiquitous surveillance. In order to better understand SHT adoption trends despite concerns, exploring the conflict between user perceptions of privacy, understanding of SHT data collection purposes, risks and benefits, and home safety, is essential.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".