Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
LLM-governed social robots increasingly decide who receives real-world assistance first. As prioritization norms vary across cultures by age, status, and group size, failure to calibrate pluralistically can scale into unequal access. Yet LLM moral audits remain English-centered, rarely test embodied contexts, leaving pluralistic calibration as an urgent diagnostic gap amid intensifying LLM-robot deployment. We introduce a gradient-based audit framework for multilingual evaluation of LLM moral trade-off behavior against cultural preference gradients. Grounded in nine cross-domain social robotics reviews (>8,000 papers), we derive symmetry-controlled scenarios across care, education, and se...
| Authors | Carmen Ng, Gjergji Kasneci |
|---|---|
| Primary category | cs.RO |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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