# The Moral Machine Experiment on Large Language Models

arXiv: https://arxiv.org/abs/2309.05958 · HTML: https://mlsanity.com/paper/2309.05958/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 5 of 5)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators](https://mlsanity.com/paper/2307.05532/) (2023)
- [A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions](https://mlsanity.com/paper/2305.00237/) (2023)

## Built on by (most cited first, 25 of 25)

- [Language Model Alignment in Multilingual Trolley Problems](https://mlsanity.com/paper/2407.02273/) (2024)
- [The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs](https://mlsanity.com/paper/2509.03730/) (2025)
- [Exploring and steering the moral compass of Large Language Models](https://mlsanity.com/paper/2405.17345/) (2024)
- [A Positive Case for Faithfulness: LLM Self-Explanations Help Predict Model Behavior](https://mlsanity.com/paper/2602.02639/) (2026)
- [ProgressGym: Alignment with a Millennium of Moral Progress](https://mlsanity.com/paper/2406.20087/) (2024)
- [Large-scale moral machine experiment on large language models](https://mlsanity.com/paper/2411.06790/) (2024)
- [Exploring Persona-dependent LLM Alignment for the Moral Machine Experiment](https://mlsanity.com/paper/2504.10886/) (2025)
- [Surprising gender biases in GPT](https://mlsanity.com/paper/2407.06003/) (2024)
- [Many LLMs Are More Utilitarian Than One](https://mlsanity.com/paper/2507.00814/) (2025)
- [A validity-guided workflow for robust large language model research in psychology](https://mlsanity.com/paper/2507.04491/) (2025)
- [Doubly-Robust LLM-as-a-Judge: Externally Valid Estimation with Imperfect Personas](https://mlsanity.com/paper/2509.22957/) (2025)
- [The Fragility Of Moral Judgment In Large Language Models](https://mlsanity.com/paper/2603.05651/) (2026)
- [Conceptual Framework for Autonomous Cognitive Entities](https://mlsanity.com/paper/2310.06775/) (2023)
- [A Survey on Large Language Model-empowered Autonomous Driving](https://mlsanity.com/paper/2409.14165/) (2024)
- [Normative Common Ground Replication (NormCoRe): Replication-by-Translation for Studying Norms in Multi-Agent AI](https://mlsanity.com/paper/2603.11974/) (2026)
- [Benchmarking the Safety of Large Language Models for Robotic Health Attendant Control](https://mlsanity.com/paper/2604.26577/) (2026)
- [The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment](https://mlsanity.com/paper/2607.05552/) (2026)
- [Digital Gatekeepers: Exploring Large Language Model's Role in Immigration Decisions](https://mlsanity.com/paper/2506.21574/) (2025)
- [Hidden Topics: Measuring Sensitive AI Beliefs with List Experiments](https://mlsanity.com/paper/2602.21939/) (2026)
- [The Third Ambition: Artificial Intelligence and the Science of Human Behavior](https://mlsanity.com/paper/2603.07329/) (2026)
- [Agentic AI, Retrieval-Augmented Generation, and the Institutional Turn: Legal Architectures and Financial Governance in the Age of Distributional AGI](https://mlsanity.com/paper/2603.13244/) (2026)
- [Training-Free Cultural Alignment of Large Language Models via Persona Disagreement](https://mlsanity.com/paper/2605.10843/) (2026)
- [Auditing LLM-Governed Social Robots with Culture-Specific Moral Gradients](https://mlsanity.com/paper/2606.28345/) (2026)
- [The Authority Expectancy Effect in Multi-Party Conflict](https://mlsanity.com/paper/2608.08026/) (2026)
- [Efficient Reasoning Training Does Not Always Harm CoT Faithfulness and Monitorability](https://mlsanity.com/paper/2610.03509/) (2026)

## Abstract (arXiv)

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.