分析大模型代理系统的责任归属问题，为安全设计提供依据。
Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective
- 从委托-代理视角解析大模型代理的责任风险
- 指出治理技术需提升可解释性与行为可控性
- 适合关注AI安全与法律合规的研究者参考
由大语言模型驱动的代理系统正变得日益复杂和自主。其不断增长的自主性与广泛应用场景引发了对有效治理、监控与控制机制的关注。基于新兴的代理市场格局，本文从委托-代理关系出发，分析大模型代理及其扩展系统在授权使用中可能引发的责任问题。研究补充了现有基于风险的人工智能代理研究，覆盖委托-代理关系的关键方面及其部署后的潜在后果。同时，我们提出技术治理方法的发展方向，包括可解释性与行为评估、奖励与冲突管理，以及通过原则化设计检测与应急机制来缓解错位与不当行为。通过揭示大模型代理系统中的核心责任难题，本文旨在推动系统设计、审计与溯源，增强透明度与责任认定能力。
原文摘要 · Abstract (English)
Agentic systems powered by large language models (LLMs) are becoming progressively more complex and capable. Their increasing agency and expanding deployment settings attract growing attention to effective governance policies, monitoring, and control protocols. Based on the emerging landscape of the agentic market, we analyze potential liability issues arising from the delegated use of LLM agents and their extended systems through a principal-agent perspective. Our analysis complements existing risk-based studies on artificial agency and covers the spectrum of important aspects of the principal-agent relationship and their potential consequences at deployment. Furthermore, we motivate method developments for technical governance along the directions of interpretability and behavior evaluations, reward and conflict management, and the mitigation of misalignment and misconduct through principled engineering of detection and fail-safe mechanisms. By illustrating the outstanding issues in AI liability for LLM-based agentic systems, we aim to inform the system design, auditing, and tracing to enhance transparency and liability attribution.
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