RoboEthics in COVID-19: A Case Study in Dentistry
Bibliographic record
Abstract
<p>The COVID-19 pandemic has caused dramatic effects on the healthcare system, businesses, and education. In many countries, businesses were shut down, universities and schools had to cancel in-person classes, and many workers had to work remotely and socially distance in order to prevent the spread of the virus. These measures opened the door for technologies such as robotics and artificial intelligence to play an important role in minimizing the negative effects of such closures. There have been many efforts in the design and development of robotic systems for applications such as disinfection and eldercare. Healthcare education has seen a lot of potential in simulation robots, which offer valuable opportunities for remote learning during the pandemic. However, there are ethical considerations that need to be deliberated in the design and development of such systems. In this paper, we discuss the principles of roboethics and how these can be applied in the new era of COVID-19. We focus on identifying the most relevant ethical principles and apply them to a case study in dentistry education. DenTeach was developed as a portable device that uses sensors and computer simulation to make dental education more efficient. DenTeach makes remote instruction possible by allowing students to learn and practice dental procedures from home. We evaluate DenTeach on the principles of data, common good, and safety, and highlight the importance of roboethics in Canada. The principles identified in this paper can inform researchers and educational institutions considering implementing robots in their curriculum.</p> <p> </p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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; a candidate call from one teacher head, 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".