Despite AI's significant growth, its "black box" nature creates challenges in
generating adequate trust. Thus, it is seldom utilized as a standalone unit in
IoT high-risk applications, such as critical industrial infrastructures,
medical systems, and financial applications, etc. Explainable AI (XAI) has
emerged to help with this problem. However, designing appropriately fast and
accurate XAI is still challenging, especially in numerical applications. Here,
we propose a universal XAI model named Transparency Relying Upon Statistical
Theory (TRUST), which is model-agnostic, high-performing, and suitable for
numerical applications. Simply put, TRUST XAI models the statistical behavior
of the AI's outputs in an AI-based system. Factor analysis is used to transform
the input features into a new set of latent variables. We use mutual
information to rank these variables and pick only the most influential ones on
the AI's outputs and call them "representatives" of the classes. Then we use
multi-modal Gaussian distributions to determine the likelihood of any new
sample belonging to each class. We demonstrate the effectiveness of TRUST in a
case study on cybersecurity of the industrial Internet of things (IIoT) using
three different cybersecurity datasets. As IIoT is a prominent application that
deals with numerical data. The results show that TRUST XAI provides
explanations for new random samples with an average success rate of 98%.
Compared with LIME, a popular XAI model, TRUST is shown to be superior in the
context of performance, speed, and the method of explainability. In the end, we
also show how TRUST is explained to the user.

提出了一种通用的解释型 AI 模型 TRUST，采用因子分析、互信息和多模态高斯分布等方法，用于解释海量数字数据应用中黑盒子型 AI 的输出，案例研究表明该模型在工业物联网网络安全领域表现良好，对新的样本的解释成功率平均可达到 98%，且在性能、速度和解释方式等方面优于流行的 LIME 模型。