The Moral Machine Experiment on Large Language Models
arXiv:2309.05958 · doi:10.1098/rsos.231393
Abstract
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.
12 pages, 2 Figures
References in corpus (6)
- Llama 2: Open Foundation and Fine-Tuned Chat Models
- Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators
- A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions
- LLM4Drive: A Survey of Large Language Models for Autonomous Driving
- Moral Dilemmas for Moral Machines
- The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
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- Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients
- Scaling Laws for Moral Machine Judgment in Large Language Models