Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine
Explainable Artificial Intelligence for 6G: Improving Trust between Human and Machine
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DOI：
10.1109/mcom.001.2000050
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发表时间：
2020-06-01
影响因子：
11.2
通讯作者：
Guo, Weisi
中科院分区：
文献类型：
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作者：
Guo, Weisi
随着5G移动网络带来全球社会效益，6G的设计阶段已经开始。演进的5G和6G将需要复杂的人工智能来同时自动化信息传递，以实现大规模自治、人机接口和有针对性的医疗保健。信任对6G来说将变得越来越重要，因为它管理着广泛的关键任务服务。当我们从传统的依赖数学模型的优化转向依赖数据的深度学习时，我们对优化模块的洞察力和信任度降低了。这种模型可解释性的丧失意味着我们很容易受到恶意数据、糟糕的神经网络设计以及利益相关者和公众信任的丧失的影响——所有这些都有一系列的法律影响。在这篇综述中，我们概述了无线网络环境中可解释人工智能（XAI）的核心方法，包括公共和法律动机、可解释性的定义、性能与可解释性的权衡，以及XAI算法。我们的评论是基于无线PHY和MAC层优化的案例研究，并为社区提供了一个重要的研究领域。
As 5G mobile networks are bringing about global societal benefits, the design phase for 6G has started. Evolved 5G and 6G will need sophisticated AI to automate information delivery simultaneously for mass autonomy, human machine interfacing, and targeted healthcare. Trust will become increasingly critical for 6G as it manages a wide range of mission-critical services. As we migrate from traditional mathematical model-dependent optimization to data-dependent deep learning, the insight and trust we have in our optimization modules decrease. This loss of model explainability means we are vulnerable to malicious data, poor neural network design, and the loss of trust from stakeholders and the general public -- all with a range of legal implications. In this review, we outline the core methods of explainable artificial intelligence (XAI) in a wireless network setting, including public and legal motivations, definitions of explainability, performance vs. explainability trade-offs, and XAI algorithms. Our review is grounded in case studies for both wireless PHY and MAC layer optimization and provide the community with an important research area to embark upon.