Mapping Ethical Blind Spots in the Use of New Technologies to Support Elder Care
Bibliographic record
Abstract
This paper maps the ethical concerns involved in using new technologies in the care of older persons by type of participant group and technology used. The study presents data from sixty-seven participants in total from three stakeholder groups (older persons, professional caregivers, and family caregivers) who relayed their opinions on three types of technologies: wearables, ambient sensors, and social assistive robots. The interview data collected was analysed using content analysis. Participants raised ethical concerns about user control, stigma, over-reliance on technology, isolation, deception, privacy breaches, and the replacement of human care. However, these concerns were not uniformly emphasized across participant groups, and not all concerns applied to each of the three technologies. Our findings revealed a reduced ethical sensitivity and preparedness for risk in the case of certain technologies. These ethical blind spots evident in our results pertained to privacy, stigma, and deception. With technologies continuously developing, such blind spots are a cause for concern.
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How this classification was reachedexpand
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".