为自主型AI系统设计风险分级框架，助力企业与政府监管
TrustX Agent Risk Classification Framework (ARC): Risk-Tiering Internally Created Agentic AI Systems
- 基于12维评分体系量化自主AI风险，结合现有治理框架
- 输出三级治理建议，匹配不同风险层级的控制措施
- 提供编码助手扩展，适配自主AI特有复杂性
自主型AI系统在企业和公共部门的广泛应用，已超出通用AI风险框架的治理能力。本文提出可信度代理风险分类框架（TrustX Agent Risk Classification Framework, ARC），一个结构化、可复现的评估工具，适用于七类自主型AI系统，其基础源自既有AI治理框架。该框架核心为12维评分量表，能稳健量化风险；结合GPA + IAT分类模型与五级自主性框架，生成三级治理输出，并配套映射控制建议。此外，还引入专用编码助手扩展，以应对此类系统的特殊复杂性。通过实例演示框架实际应用。本框架面向AI治理实践者、风控人员、开发者及监管机构，将持续迭代优化。社区可访问交互式框架：https://arc.responsible.ai/
原文摘要 · Abstract (English)
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/
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