Large-scale moral machine experiment on large language models

Abstract

The rapid advancement of Large Language Models (LLMs) and their potential integration into autonomous driving systems necessitates understanding their moral decision-making capabilities. While our previous study examined four prominent LLMs using the Moral Machine experimental framework, the dynamic landscape of LLM development demands a more comprehensive analysis. Here, we evaluate moral judgments across 52 different LLMs, including multiple versions of proprietary models (GPT, Claude, Gemini) and open-source alternatives (Llama, Gemma), to assess their alignment with human moral preferences in autonomous driving scenarios. Using a conjoint analysis framework, we evaluated how closely LLM responses aligned with human preferences in ethical dilemmas and examined the effects of model size, updates, and architecture. Results showed that proprietary models and open-source models exceeding 10 billion parameters demonstrated relatively close alignment with human judgments, with a significant negative correlation between model size and distance from human judgments in open-source models. However, model updates did not consistently improve alignment with human preferences, and many LLMs showed excessive emphasis on specific ethical principles. These findings suggest that while increasing model size may naturally lead to more human-like moral judgments, practical implementation in autonomous driving systems requires careful consideration of the trade-off between judgment quality and computational efficiency. Our comprehensive analysis provides crucial insights for the ethical design of autonomous systems and highlights the importance of considering cultural contexts in AI moral decision-making.

21 pages, 6 figures

References in corpus (15)

• The Ethics of AI Ethics -- An Evaluation of Guidelines
• The social dilemma of autonomous vehicles
• Gemini: A Family of Highly Capable Multimodal Models
• Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
• Gemma: Open Models Based on Gemini Research and Technology
• Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
• Gemma 2: Improving Open Language Models at a Practical Size
• Explainable Artificial Intelligence for Autonomous Driving: A Comprehensive Overview and Field Guide for Future Research Directions
• A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions
• The Moral Machine Experiment on Large Language Models
• LLM4Drive: A Survey of Large Language Models for Autonomous Driving
• Moral Dilemmas for Moral Machines
• Harnessing Large Language Models Over Transformer Models for Detecting Bengali Depressive Social Media Text: A Comprehensive Study
• Knowing When to Ask -- Bridging Large Language Models and Data
• Language Model Alignment in Multilingual Trolley Problems

Cited by in corpus (3)

• Large Language Models as Psychological Simulators: A Methodological Guide
• From Prompts to Constructs: A Dual-Validity Framework for Large Language Model Research in Psychology
• Scaling Laws for Moral Machine Judgment in Large Language Models