大规模语言模型上的大规模道德机器实验
Large-scale moral machine experiment on large language models
AI总结：
本研究评估52个LLM在自动驾驶道德困境中与人类判断的一致性，发现模型规模增大可提升一致性，但更新未必改善，为自主系统伦理设计提供参考。
摘要：
大型语言模型（LLMs）的快速发展及其在自动驾驶系统中的潜在整合，使得理解其道德决策能力变得至关重要。虽然我们之前的研究使用道德机器实验框架考察了四个著名的LLMs，但LLM发展的动态格局要求进行更全面的分析。在此，我们评估了52个不同LLMs的道德判断，包括多个版本的专有模型（GPT、Claude、Gemini）和开源替代模型（Llama、Gemma），以评估它们在自动驾驶场景中与人类道德偏好的一致性。使用联合分析框架，我们评估了LLM响应在伦理困境中与人类偏好的接近程度，并考察了模型规模、更新和架构的影响。结果表明，专有模型和超过100亿参数的开源模型与人类判断表现出相对接近的一致性，且在开源模型中，模型规模与人类判断距离之间存在显著的负相关。然而，模型更新并未持续改善与人类偏好的一致性，且许多LLM表现出对特定伦理原则的过度强调。这些发现表明，虽然增加模型规模可能自然导致更类人的道德判断，但在自动驾驶系统中的实际实施需要仔细考虑判断质量与计算效率之间的权衡。我们的全面分析为自主系统的伦理设计提供了关键见解，并强调了在AI道德决策中考虑文化背景的重要性。
英文摘要：
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.
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