Multilingual Trolley Problems for Language Models

Zhijing Jin, Max Kleiman-Weiner, Giorgio Piatti, Sydney Levine, Jiarui Liu, Fernando Gonzalez Adauto, Francesco Ortu, András Strausz, Mrinmaya Sachan, Rada Mihalcea, Yejin Choi, Bernhard Schölkopf

Keywords: language/cultural bias analysis

Abstract:

As large language models (LLMs) are deployed in more and more real-world
situations, it is crucial to understand their decision-making when faced with moral
dilemmas. Inspired by a large-scale cross-cultural study of human moral prefer-
ences, “The Moral Machine Experiment”, we set up the same set of moral choices
for LLMs. We translate 1K vignettes of moral dilemmas – parametrically varied
across key axes – into 100+ languages, and reveal the preferences of LLMs in
each of these languages. We then compare the responses of LLMs to that of hu-
man speakers of those languages, harnessing a dataset of 40 million human moral
judgments. We discover that LLMs are more aligned with human preferences in
languages such as English, Korean, Hungarian, and Chinese, but less aligned in
languages such as Hindi and Somali (in Africa). Moreover, we characterize the
explanations LLMs give for their moral choices and find that fairness is the most
dominant supporting reason behind GPT-4’s decisions and utilitarianism by GPT-3.
We also discover “language inequality” (which we define as the model’s different
development levels in different languages) in a series of meta-properties of moral
decision making.

Submission Number: 69