  Large language models (LLMs) increasingly find their way into the most
diverse areas of our everyday lives. They indirectly influence people's
decisions or opinions through their daily use. Therefore, understanding how and
which moral judgements these LLMs make is crucial. However, morality is not
universal and depends on the cultural background. This raises the question of
whether these cultural preferences are also reflected in LLMs when prompted in
different languages or whether moral decision-making is consistent across
different languages. So far, most research has focused on investigating the
inherent values of LLMs in English. While a few works conduct multilingual
analyses of moral bias in LLMs in a multilingual setting, these analyses do not
go beyond atomic actions. To the best of our knowledge, a multilingual analysis
of moral bias in dilemmas has not yet been conducted.
  To address this, our paper builds on the moral machine experiment (MME) to
investigate the moral preferences of five LLMs, Falcon, Gemini, Llama, GPT, and
MPT, in a multilingual setting and compares them with the preferences collected
from humans belonging to different cultures. To accomplish this, we generate
6500 scenarios of the MME and prompt the models in ten languages on which
action to take. Our analysis reveals that all LLMs inhibit different moral
biases to some degree and that they not only differ from the human preferences
but also across multiple languages within the models themselves. Moreover, we
find that almost all models, particularly Llama 3, divert greatly from human
values and, for instance, prefer saving fewer people over saving more.
