为AI网络管理中的责任归属问题设计智能审计系统
Closing the Responsibility Gap in AI-based Network Management: An Intelligent Audit System Approach
- 用深度强化学习与机器学习模型识别故障责任方
- DRL模型识别准确率达96%，ML模型预测网络状态准确率83%
- 适合关注AI问责、网络运维安全的研究者和工程师
现有网络架构通过人工智能驱动的管理工具实现了更低的中断率和更高的用户体验质量（QoE）。这些AI管理系统能自动响应网络状态变化，降低运营商成本并提升整体性能。然而，引入AI也带来了无人监督、隐私侵犯、算法偏见和模型误差等挑战。当AI代理未能应对这些问题时，应由其自身承担相应责任，而非整个网络。为此，本文提出一个包含深度强化学习（DRL）与机器学习（ML）模型的框架，用于识别并量化涉及网络决策的AI管理代理的责任值。通过模拟网络运行参数训练该框架，测试结果显示，DRL模型在识别责任代理方面达到96%准确率，基于梯度下降的ML模型在预测网络状态方面达到83%准确率。
原文摘要 · Abstract (English)
Existing network paradigms have achieved lower downtime as well as a higher Quality of Experience (QoE) through the use of Artificial Intelligence (AI)-based network management tools. These AI management systems, allow for automatic responses to changes in network conditions, lowering operation costs for operators, and improving overall performance. While adopting AI-based management tools enhance the overall network performance, it also introduce challenges such as removing human supervision, privacy violations, algorithmic bias, and model inaccuracies. Furthermore, AI-based agents that fail to address these challenges should be culpable themselves rather than the network as a whole. To address this accountability gap, a framework consisting of a Deep Reinforcement Learning (DRL) model and a Machine Learning (ML) model is proposed to identify and assign numerical values of responsibility to the AI-based management agents involved in any decision-making regarding the network conditions, which eventually affects the end-user. A simulation environment was created for the framework to be trained using simulated network operation parameters. The DRL model had a 96% accuracy during testing for identifying the AI-based management agents, while the ML model using gradient descent learned the network conditions at an 83% accuracy during testing.
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