END-USER PERSPECTIVES ON POLICIES FOR SOCIAL ROBOTICS WITH AGING APPLICATIONS
Bibliographic record
Abstract
Abstract Social robots, including those with artificial intelligence (AI)-enabled functionalities, are promising in their potential to support brain health in older adults in both home and healthcare environments, but their benefits must be weighed against ethical considerations and risks. To characterize and evaluate existing guidance in this field, we conducted a content analysis of n = 47 international policies on social robotics and conducted n = 18 semi-structured interviews with lived experience experts within the dementia and care partnership community and professional experts in the field of aging and dementia. Participants viewed social robots as potentially helpful assistive technologies and agreed with existing recommendations around the needs for respect for human rights and dignity, clear and consistent regulation, and public engagement. Participants recommended that policies prioritize cost and accessibility considerations, represent the needs of minority groups, and that they center around issues specific to aging and dementia. They reported that the responsibility for social robot policy development should lie with governments and the healthcare sector and called for increased consultation with end-users and medical professionals. Results also highlighted misalignments between the priorities of the aging and dementia community members and the existing social robot policy landscape. Potential harms and challenges to adoption of social robots were discussed, including discrimination, bias, and deception. Taken together, findings from this two-phase study can inform evidence-based policy recommendation for the development and implementation of AI-enabled social robots for aging.
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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 | 0.064 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".