Aging in place with Google and Amazon Smart Speakers: Privacy and Surveillance Implications for Older Adults
Bibliographic record
Abstract
Commercial-grade smart home technologies (SHTs) such as Google and Amazon smart speakers are rising in popularity among older adults. Marketing materials claim that smart speakers can support older adults aging in place through emergency contact features, medication reminders, and digital companionship with voice assistants. As our aging population challenges strained health and senior care systems in Canada, SHTs are positioned to alleviate some of the pressure. At the same time, under surveillance capitalism, big tech companies and marketers stand to profit from collecting massive amounts of user data in attempts to predict, modify, and control behaviour through targeted advertisements. While Canadian private sector privacy legislation hinges on meaningful user consent for data collection, obtaining such consent can prove difficult for smart speaker users in general, especially for older adults with limited technological experience. Further, little is known about the types of ads that follow older adults around the web through programmatic advertising. To better understand the dynamics between Google, Amazon, and older adult smart speaker users, this dissertation asks the following: How are smart speakers marketed to older adults and care partners, how are they used, and what are the implications for privacy, surveillance, and aging in place in Canada? A multi-methods approach is used to answer this question by including the voices of older adult smart speaker users alongside interviews with relevant experts in technology, privacy, and aging. This study also relies on a qualitative thematic analysis of marketing materials, documentary analyses of privacy policies and relevant legislation, and an algorithmic audit to further explore the relationship between older adults’ privacy, autonomy, and targeted advertising. Alongside user education programs, it concludes with suggestions for user-centric design and data justice as a regulatory approach that supports user privacy and autonomy while challenging the potential for bias.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".