Roboethics in Remote Human Interactions and Rehabilitative Therapeutics
Bibliographic record
Abstract
Global aging, the need for continuous high-quality services, and the recent COVID-19 pandemic have caused many sectors to reduce in-person contact between individuals where possible, and to instead use remote communication technology. With increased robot use replacing in-person interventions, ethical considerations arise. The benefits of robotic technology must be weighed against the possibility of harm to humans. The first rules of roboethics were written by Isaac Asimov in 1942, but these pre-date the range of robotic applications used today. There is no central agency for oversight in Canada, although universities and funding organizations have developed guidelines. Roboethical considerations include data use and privacy, the common good, and safety. Humans may potentially form emotional bonds with robots. Loss of jobs to automation is also a concern, particularly when many workers have suffered job losses due to the pandemic. Some health care services have switched to a remote care model, to reduce the spread of COVID-19. Remote hand rehabilitation of patients with brain injury is possible with iManus. Using a portable smart-glove technology, patients practice rehabilitative hand movements that are analyzed remotely through a sensor that offers instantaneous feedback. A therapist monitors their patient’s progress remotely and designs individualized programs. This technology benefits humanity by improving access to care for patients globally. The importance of data security and privacy is emphasized. As patient progress will be monitored by a human (a therapist), the safety of iManus is not a concern. The principles discussed in this paper can help researchers and engineers design ethical robotics in remote health care settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".