The Emerging Policy and Ethics of Human Robot Interaction
Bibliographic record
Abstract
As robotics technology forays into our daily lives, research, industry, and government professionals in the field of human-robot interaction (HRI) in must grapple with significant ethical, legal, and normative questions. Many leaders in the field have suggested that "the time is now" to start drafting ethical and policy guidelines for our community to guide us forward into this new era of robots in human social spaces. However, thus far, discussions have been skewed toward the technology side or policy side, with few opportunities for cross-disciplinary conversation, creating problems for the community. Policy researchers can be concerned about robot capabilities that are scientifically unlikely to ever come to fruition (like the singularity), and technologists can be vehemently opposed to ethics and policy encroaching on their professional space, concerned it will impede their work. This workshop aims to build a cross-disciplinary bridge that will ensure mutual education and grounding, and has three main goals: 1) Cultivate a multidisciplinary network of scholars who might not otherwise have the opportunity to meet and collaborate, 2) Serve as a forum for guided discussion of relevant topics that have emerged as pressing ethical and policy issues in HRI, 3) Create a working consensus document for the community that will be shared broadly.
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 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 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.069 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.005 | 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".