Can We Trust AI in 6G? Verifiable and Auditable AI-Driven Trustworthy Wireless Networks

2026-07-28

AI Summary

This work addresses the critical lack of verifiability and auditability of artificial intelligence (AI) in current 6G networks, which undermines confidence in the correctness and regulatory compliance of AI-driven decisions and impedes trustworthy deployment in mission-critical communication infrastructure. To bridge this gap, the paper proposes a mechanistic auditing framework that uniquely integrates mechanistic interpretability with 3GPP protocol specifications. By leveraging internal representation monitoring, causal analysis, and dedicated verification agents, the approach establishes an “audit-native” network architecture. This architecture enables both pre-deployment certification and continuous runtime auditing, operationalized through a principled three-step auditing methodology. The study thus provides a comprehensive foundation—including a theoretical framework, a system prototype, and a practical implementation pathway—for standardized validation of AI functionalities in 6G systems.

Abstract

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.

Problem

• trustworthy AI
• verifiability
• auditable AI
• 6G networks
• AI transparency

Innovation

• mechanistic auditing
• verifiable AI
• trustworthy wireless networks
• audit-native architecture
• 3GPP compliance