  As large language models (LLMs) become more deeply integrated into various
sectors, understanding how they make moral judgments has become crucial,
particularly in the realm of autonomous driving. This study utilized the Moral
Machine framework to investigate the ethical decision-making tendencies of
prominent LLMs, including GPT-3.5, GPT-4, PaLM 2, and Llama 2, comparing their
responses to human preferences. While LLMs' and humans' preferences such as
prioritizing humans over pets and favoring saving more lives are broadly
aligned, PaLM 2 and Llama 2, especially, evidence distinct deviations.
Additionally, despite the qualitative similarities between the LLM and human
preferences, there are significant quantitative disparities, suggesting that
LLMs might lean toward more uncompromising decisions, compared to the milder
inclinations of humans. These insights elucidate the ethical frameworks of LLMs
and their potential implications for autonomous driving.
