Designing user-centered policy for social robotics: policy analysis and consultation with the aging and dementia community
Bibliographic record
Abstract
PURPOSE: Social robots are a promising assistive technology to support older adults in home and healthcare environments. Engaging end-users in all stages of social robot research, development, and deployment is critical to adoption. However, the voices of end-users are missing from policies about social robots. This work consults with end-users of social robots to capture their perspectives on social robot policies and co-create expert-driven policy recommendations to guide the future implementation of social robots for aging. MATERIALS AND METHODS: = 11) perspectives to capture their opinions about social robot policies. RESULTS: Our analysis highlights alignments between social robot policy recommendations and perspectives of the dementia community including upholding respect for human rights and dignity, the need for clear and consistent regulation, and the need for public engagement. Participants further recommended that policies should prioritize cost and accessibility considerations and focus on aging- and dementia-specific considerations. Participants reported that the responsibility for social robot policy development lay primarily with governments and the healthcare sector. Increased consultation with end-users, minority groups and medical professionals was suggested for future policy development. CONCLUSION: Findings contribute to the ethical co-creation of social robots as assistive technologies for older adults and provide actionable steps for the development of policies that reflect the values and perspectives of end-users.
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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 | 0.202 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 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".