AI 中文总结
针对智能体人工智能系统超出通用风险框架治理能力的问题，提出TrustX智能体风险分类框架（ARC），利用十二维度评分标准等组件量化风险，输出三层治理结果，为相关人员提供工具，且会持续迭代完善。
AI 中文摘要
在企业和公共部门环境中，智能体人工智能系统的迅速扩散，已超出通用人工智能风险框架对其进行分类和治理的能力。本文介绍了TrustX智能体风险分类框架，这是一种结构化、可重复的工具，可应用于七种智能体人工智能系统，且基于现有的人工智能治理框架。该框架核心是一个十二维度的评分标准，能有力地量化风险。此标准与其他组件相结合，如GPA + IAT分类模型和从现有文献中得出的五级自主性框架。这些输入产生带有映射控制建议的三层治理输出。还包括一个专门的编码助手扩展，以处理这类智能体人工智能系统的细微差别。然后通过一个示例展示框架的实际应用。ARC面向人工智能治理从业者、风险官员、开发者和监管者，随着不断扩展和完善，将定期迭代。社区可通过链接访问交互式框架。
英文摘要
The proliferation of agentic AI systems across enterprise and public-sector contexts has outpaced the capacity of general-purpose AI risk frameworks to classify and govern them. In this paper, we introduce the TrustX Agent Risk Classification Framework, a structured, repeatable instrument that can be applied to seven types of agentic AI systems and is grounded in foundational pre-existing AI governance frameworks. At the core of the framework is a twelve-dimension scoring rubric that robustly quantifies the risk. This rubric is combined with other components, such as the GPA + IAT classification model and the five-level autonomy framework derived from existing literature. These inputs produce a three-tier governance output with mapped control recommendations. A specialised Coding Assistant extension is also included to account for nuances specific to this type of agentic AI system. We then use an illustrative example to show our framework in practice. ARC is intended for AI governance practitioners, risk officers, developers, and regulators, and it will regularly undergo iteration as we continue to expand it and make it more robust. The community can access the interactive framework here: https://arc.responsible.ai/
CommentsThis is a working paper on our risk classification tool, with iterations currently underway
