Welfare Technology Developers’ Views Concerning Responsible Innovation and Implementation of Care Robots in Ireland and Japan
Bibliographic record
Abstract
Care robots are now seen as part of the solution to global aging. This article asks: how has responsible robotics been perceived? What are the missing elements that would enable responsible robotics (in research, development, and wider use) in different jurisdictions? In order to answer these questions, the article explores the views of welfare technology (WT) developers concerning the current state of robotics development and use in care settings in Ireland and Japan. Semi-structured in-depth interviews were conducted with 14 technology developers in total. The findings indicate that technology developers strongly believe that the use of care robots and WTs would strengthen the long-term care systems in both countries, if ethical and other aspects are taken into consideration. The uniform long-term care system can facilitate the top-down introduction of care robots, but a mismatch can be widened between users’ needs and the solutions that WTs can provide. The inclusion of WTs in professional curricula and training programs and changing the often-skewed media representation of AI and robotics were presented as a possible way forward.
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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 | 0.014 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".