AI 中文总结
针对6G中AI在无线网络应用的信任问题，提出基于3GPP规范的机械审计方法与原生审计网络架构，支持AI功能的部署前认证和运行时审计，为AI驱动的可信无线网络提供解决方案。
AI 中文摘要
移动网络运营商正越来越多地探索使用人工智能（AI）来自动化复杂的网络任务，例如小区选择和移动性管理。一个根本问题随之产生：目前无法验证AI功能是否做出了正确的决策或基于正确的原因，而非通过不可靠的捷径得到看似正确的答案。在安全关键和注重弹性的基础设施中，这种透明度的缺失对AI技术在无线网络中的广泛应用构成了重大挑战。本文提出一种机械审计方法：检查功能的内部表示并对照机器可验证的3GPP规范进行核查。具体而言，我们提出了一个通用的三步审计原则，即定位协议相关特征、验证其因果作用、诊断适应性如何改变其使用，全程以公开的可解释性和电信研究为基础。我们提出了一种原生审计的网络架构，其中专用的验证代理持续检查网络中AI功能的推理，支持部署前认证和运行时审计。我们还讨论了该架构的实现方式、所需的数据与基准，以及机械审计进入电信实践和标准化之前仍存在的开放挑战。
英文摘要
Mobile network operators are increasingly exploring the use of artificial intelligence (AI) to automate complex network tasks, such as cell selection and mobility management. A fundamental problem arises: there is currently no way to verify that an AI function is making the right decisions or for the right reasons, rather than arriving at correct-looking answers through unreliable shortcuts. In safety-critical and resilience-focused infrastructure, this lack of transparency poses a significant challenge to the widespread adoption of AI technologies in wireless networks. In this paper, we propose a mechanical auditing approach: inspecting a function's internal representations and checking them against machine-verifiable 3GPP specifications. Specifically, we set out a general three-step auditing principle that locates protocol-relevant features, verifies their causal role, and diagnoses how adaptation reshapes their use, grounding it throughout publicly available interpretability and telecommunications research. We present an audit-native network architecture in which a dedicated verification agent continuously checks the reasoning of AI functions in networks, supporting both predeployment certification and runtime auditing. We also discuss how it could be realised, the data and benchmarks, as well as the open challenges that remain before mechanistic auditing can enter telecommunications practice and standardisation.
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