Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
A new study introduces a gradient-based audit framework to evaluate the moral trade-off behavior of LLM-governed social robots across different cultures. The research finds persistent culturally asymmetric gradient tracking failures, with quality calibration nearly twice as strong for Western-language decisions as for Chinese and Japanese, and high determinism in majority-first trade-offs erasing cross-cultural gradients. The study calls for multilingual, pluralistic audits before deployment.
[2606.28345] Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
[Submitted on 2 Jun 2026]
Title:Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
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Abstract: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 services, translating the Moral Machine Experiment's "whom to spare" into "whom to assist first" dilemmas with preserved identity trade-offs (many vs. few; young vs. old; higher vs. lower status). We audit four LLMs across four country-language pairs in four prompting regimes (57,600 decisions), benchmarked against country-specific MME preference gradients. Ordinal concordance tests whether models differentiate cultural contexts; a governance typology maps vulnerabilities in gradient differentiation, directional tendency, and deliberation. We find persistent, culturally asymmetric gradient tracking failures that prompting alone cannot reliably correct: quality calibration is nearly twice as strong for Western-language decisions as for Chinese and Japanese; high determinism in majority-first trade-offs often erases cross-cultural gradients; partial sensitivity to age- and status-based norms risks sidelining minorities. Prompting effects are uneven; only contrastive exemplars yield consistent gains, while reasoning-only prompts can worsen tracking. Our results motivate multilingual, pluralistic audits as an LLM-robot pre-deployment gate and suggest model factors are a more robust lever than prompting alone.
Comments: Accepted for publication in Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency (FAccT '26)
Subjects:
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY)
Cite as: arXiv:2606.28345 [cs.RO]
(or arXiv:2606.28345v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.28345
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1145/3805689.3812366
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Submission history
From: Carmen Ng [view email] [v1] Tue, 2 Jun 2026 10:22:53 UTC (1,644 KB)
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