ArticleRoyal Society open science2024
The moral machine experiment on large language models.
Article in Royal Society open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- A validity-guided workflow for robust large language model research in psychology.Behavior research methods · 2026Article
- Does a Smarter ChatGPT Become More Utilitarian?Science and engineering ethics · 2025Article
- ChatGPT does not replicate human moral judgments: the importance of examining metrics beyond correlation to assess agreement.Scientific reports · 2025Article
- Large language models show amplified cognitive biases in moral decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Robustness of large language models in moral judgements.Royal Society open science · 2025Article
- Article
- Comparing AI and human moral reasoning: context-sensitive patterns beyond utilitarian bias.Frontiers in artificial intelligence · 2025Article
- Article
- The moral machine experiment on large language models.Royal Society open science · 2024Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
As large language models (LLMs) have become more deeply integrated into various sectors, understanding how they make moral judgements has become crucial, particularly in the realm of autonomous driving. This study used 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, to compare their responses with human preferences. While LLMs' and humans' preferences such as prioritizing humans over pets and favouring 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 with the milder inclinations of humans. These insights elucidate the ethical frameworks of LLMs and their potential implications for autonomous driving.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.