The Moral Machine Experiment on Large Language Models
This addresses the problem of understanding and aligning LLM moral judgments for developers and policymakers in autonomous driving, though it is incremental as it applies an existing framework to new models.
This study used the Moral Machine framework to analyze the ethical decision-making of large language models (LLMs) like GPT-3.5, GPT-4, PaLM 2, and Llama 2 in autonomous driving scenarios, finding that while LLMs broadly align with human preferences such as prioritizing humans over pets, PaLM 2 and Llama 2 show distinct deviations and LLMs tend toward more uncompromising decisions compared to humans.
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.