自主性经济学：适用于可保险的人工智能驱动的6G系统的实时风险索引
The Economics of Autonomy: Real-Time Risk Indexing for Insurable AI-Driven 6G Systems
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中文总结 AI 辅助
研究6G网络中传统治理框架在风险管理上的不足，提出GIRAF框架，通过机器可读运行时信号得出总风险指数，形式化验证陈旧性权衡，经模拟验证该框架能保持操作完整性并为多利益相关者责任归属等提供精算基线。
中文摘要 AI 辅助
向第六代（6G）网络的过渡将无线基础设施转变为支持车联网（V2X）、工业物联网（IIoT）和集成传感与通信（ISAC）的认知基础。在此范式中，自主智能AI在毫秒级进行编排，使传统静态治理框架在风险管理上根本不足。本文介绍了GIRAF（治理集成风险与保障框架），一个用于智能6G系统中实时风险量化和信任调制的治理即代码（GaC）框架。GIRAF从包括认知置信度、网络抖动和验证延迟等机器可读运行时信号中得出连续的总风险指数（$R_{t}$）。核心贡献是对验证陈旧性权衡的形式化，即如果计算延迟超过6G期限，安全机制会引发风险。通过模拟验证框架在保持操作完整性的同时，为6G生态系统中多利益相关者责任归属和动态保费量化提供了必要的精算基线。
英文摘要
The transition to sixth-generation (6G) networks transforms wireless infrastructure into a cognitive substrate supporting Vehicle-to-Everything (V2X), Industrial IoT (IIoT), and Integrated Sensing and Communication (ISAC). In this paradigm, autonomous agentic AI performs orchestration at millisecond scales, rendering traditional static governance frameworks fundamentally inadequate for risk management. This paper introduces GIRAF(Governance-Integrated Risk and Assurance Framework), a Governance-as-Code (GaC) framework for real-time risk quantification and trust modulation in agentic 6G systems. GIRAF derives a continuous Aggregate Risk Index ($R_{t}$) from machine-readable runtime signals, including epistemic confidence, network jitter, and verification latency. A core contribution is the formalization of the verification staleness trade-off, where safety mechanisms induce risk if computational latency exceeds 6G deadlines. We demonstrate that GIRAF identifies 'Confidence Gaps' discrepancies between agent reported certainty and environmental ground truth, triggering automated safety envelopes when conditions deteriorate. Crucially, GIRAF serves as the foundational governance groundwork and conceptual 'glue' that externalizes these technical risks into machine-readable telemetry. Through simulations with fine-tuned Large Language Models (LLMs), we validate that the framework preserves operational integrity while providing the essential actuarial baseline required for multi-stakeholder liability attribution and dynamic premium quantification in the 6G ecosystem.
发表机构
- German Research Center for Artificial Intelligence (DFKI), Germany（德国人工智能研究中心（DFKI））
- RPTU University of Kaiserslautern-Landau, Germany（莱茵-威斯伐尔大学科堡分校）
机构由 AI 辅助整理，请以论文原文为准。
