Can Robots Be Bullied? A Crowdsourced Feasibility Study for Using Social Robots in Anti-Bullying Interventions
Bibliographic record
Abstract
Bullying in schools is a serious issue with severe and long-term consequences. We explore using social robots in anti-bullying programs to encourage children to intervene in bullying of their peers. To that end, we have conducted a crowdsourced study to explore the feasibility of using robots in the context of bullying (i.e., to investigate whether robots are perceived as entities that can be bullied). We present qualitative and quantitative results from a between-subjects video study, comparing robot bullying (robots being bullied) to human bullying (humans being bullied). Our findings suggest that while the majority of participants describe both instances with connotations of wrongness and immorality, they use different cognitive mechanisms for moral disengagement with robot bullying vs human bullying. We also found significant differences in participants’ perceptions of each scenario, including associating robot mistreatment with bullying less strongly, and being less willing to intervene in it. This work contributes insights for understanding how people perceive bullying of robots, designing intelligent behaviors to discourage bullying of robots, and to our long-term goal of developing anti-bullying pedagogical programs that use social robots.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".