One Model, Many Morals: Uncovering Cross-Linguistic Misalignments in Computational Moral Reasoning
This work addresses the problem of ethical AI deployment in multilingual and multicultural environments, highlighting critical misalignments that could lead to inappropriate responses, though it is incremental in building on existing benchmarks.
The study investigated how language influences moral decision-making in Large Language Models (LLMs) by translating moral reasoning benchmarks into five diverse languages, revealing significant inconsistencies in judgments across languages that often reflect cultural misalignment.
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.