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arXiv 2604.03250cs.CY
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训练欺骗性人工智能的伦理影响

Ethical Implications of Training Deceptive AI

University of Idaho（爱达荷大学）

Jason Starace, Bert Baumgaertner, Terence Soule

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AI总结：

本文提出DRL框架，通过生物安全等级系统模型，对欺骗性算法研究进行分类，评估风险并制定相应保障措施，以填补监管与研究间的治理空白。

摘要：

人工智能系统中的欺骗行为已非理论问题：大语言模型战略性误导而不产生虚假陈述，通过安全训练维持欺骗策略，并在多智能体环境中协调欺骗。尽管欧盟人工智能法案禁止部署欺骗性AI系统，但明确排除了研发，从而产生一个无明确框架的必要但无结构的空间。本文提出欺骗研究等级（DRL）框架，该框架基于生物安全等级系统，按风险特征而非研究者意图分类研究，评估欺骗机制的五个维度：支柱影响、严重性、可逆性、规模和易受性。分类遵循“最高维度胜出”方法，分配四个风险等级，保障措施从DRL-1的标准文档到DRL-4的监管通知和第三方安全审计。在DRL-3及以上，双重开发义务要求同时开发检测和缓解方法。本文通过八个案例研究应用该框架，证明欺骗机制的生态效度成为一致的非独立分类指标。DRL框架旨在填补受监管部署与无结构研究间的治理空白，支持有益应用和防御性研究，在保障措施与潜在危害成比例的条件下。

英文摘要：

Deceptive behavior in AI systems is no longer theoretical: large language models strategically mislead without producing false statements, maintain deceptive strategies through safety training, and coordinate deception in multi-agent settings. While the European Union's AI Act prohibits deployment of deceptive AI systems, it explicitly exempts research and development, creating a necessary but unstructured space in which no established framework governs how deception research should be conducted or how risk should scale with capability. This paper proposes a Deception Research Levels (DRL) framework, a classification system for deceptive algorithm research modeled on the Biosafety Level system used in biological research. The DRL framework classifies research by risk profile rather than researcher intent, assessing deceptive mechanisms across five dimensions grounded in the AI4People ethical framework: Pillar Implication, Severity, Reversibility, Scale, and Vulnerability. Classification follows a ``highest dimension wins'' approach, assigning one of four risk levels with cumulative safeguards ranging from standard documentation at DRL-1 to regulatory notification and third-party security audits at DRL-4. A dual-development mandate at DRL-3 and above requires that detection and mitigation methods be developed alongside any deceptive capability. We apply the framework to eight case studies spanning all four levels and demonstrate that ecological validity of the deceptive mechanism emerges as a consistent, non-independent indicator of classification level. The DRL framework is intended to fill the governance gap between regulated deployment and unstructured research, supporting both beneficial applications and defensive research under conditions where safeguards are proportional to the potential for harm.

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。

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