审计生成式AI模型的伦理逻辑
Auditing the Ethical Logic of Generative AI Models
AI总结：
本文提出五维审计模型评估生成式AI的伦理推理，通过多组提示基准测试七个LLM，发现模型决策趋同但解释与优先级有差异，思维链可提升表现。
摘要：
随着生成式AI模型日益融入高风险领域，对其伦理推理能力进行稳健评估的需求变得愈发重要。本文引入了一个五维审计模型——评估分析质量、伦理考量广度、解释深度、一致性和决断力——以评估领先大型语言模型（LLMs）的伦理逻辑。借鉴应用伦理学和高级思维的传统，我们提出了一种多组提示方法，包括新颖的伦理困境，以探测模型在不同情境下的推理能力。我们对七个主要LLM进行了基准测试，发现虽然模型在伦理决策上大体趋同，但在解释严谨性和道德优先级排序上存在差异。思维链提示和推理优化模型显著提升了我们在审计指标上的表现。本研究引入了一种可扩展的AI系统伦理基准测试方法，并强调了AI在复杂决策情境中补充人类道德推理的潜力。
英文摘要：
As generative AI models become increasingly integrated into high-stakes domains, the need for robust methods to evaluate their ethical reasoning becomes increasingly important. This paper introduces a five-dimensional audit model -- assessing Analytic Quality, Breadth of Ethical Considerations, Depth of Explanation, Consistency, and Decisiveness -- to evaluate the ethical logic of leading large language models (LLMs). Drawing on traditions from applied ethics and higher-order thinking, we present a multi-battery prompt approach, including novel ethical dilemmas, to probe the models' reasoning across diverse contexts. We benchmark seven major LLMs finding that while models generally converge on ethical decisions, they vary in explanatory rigor and moral prioritization. Chain-of-Thought prompting and reasoning-optimized models significantly enhance performance on our audit metrics. This study introduces a scalable methodology for ethical benchmarking of AI systems and highlights the potential for AI to complement human moral reasoning in complex decision-making contexts.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
