The Role of Social Norms in Human–Robot Interaction: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As robots integrate more into daily life, socially aware robots with specific social attributes and behaviors are necessary. This review aims to explore how social norms in Human–Robot Interaction (HRI) impact robot design and human perception. We searched for relevant articles in the following databases: ACM Digital Library, IEEE Digital Library, Scopus, Springer Link, and PsycINFO. After applying inclusion and exclusion criteria, a final set of 69 articles were included in the review. These articles were categorized based on whether they examined norm conformity or norm violations, and were further sorted into 12 categorical norm labels to assist in analysis and comparison. By examining the existing literature, this review uncovers how social norms impact aspects of HRIs like trust, acceptance, and comfort while highlighting the importance of aligning robot design with user expectations. It reveals design challenges such as accounting for cultural variations, context-specific norms, and evolving norms over time. Addressing these challenges has the potential to improve user experiences, promote broader acceptance of robots, and foster successful integration of robots into various domains. The findings contribute to the ongoing discussion on the role of social norms in HRI, offering valuable insights and a foundation for future research.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it