The Role of Social Norms in Human–Robot Interaction: A Systematic Review
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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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".