One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning

• URL: http://arxiv.org/abs/2509.21443v1
• Date: Thu, 25 Sep 2025 19:14:17 GMT
• Title: One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning
• Authors: Sualeha Farid, Jayden Lin, Zean Chen, Shivani Kumar, David Jurgens,
• Abstract summary: We investigate how language mediates moral decision-making in Large Language Models (LLMs)<n>Our analysis reveals significant inconsistencies in LLMs' moral judgments across languages, often reflecting cultural misalignment.<n>We distill our insights into a structured typology of moral reasoning errors that calls for more culturally-aware AI.
• Score: 23.56514813420256
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) are increasingly deployed in multilingual and multicultural environments where moral reasoning is essential for generating ethically appropriate responses. Yet, the dominant pretraining of LLMs on English-language data raises critical concerns about their ability to generalize judgments across diverse linguistic and cultural contexts. In this work, we systematically investigate how language mediates moral decision-making in LLMs. We translate two established moral reasoning benchmarks into five culturally and typologically diverse languages, enabling multilingual zero-shot evaluation. Our analysis reveals significant inconsistencies in LLMs' moral judgments across languages, often reflecting cultural misalignment. Through a combination of carefully constructed research questions, we uncover the underlying drivers of these disparities, ranging from disagreements to reasoning strategies employed by LLMs. Finally, through a case study, we link the role of pretraining data in shaping an LLM's moral compass. Through this work, we distill our insights into a structured typology of moral reasoning errors that calls for more culturally-aware AI.

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