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.
LENS-XAI: Redefining Lightweight and Explainable Network Security through Knowledge Distillation and Variational Autoencoders for Scalable Intrusion Detection in Cybersecurity
Kırıkkale University（克尔克卡拉大学）；University College Dublin（都柏林大学学院）；CeADAR；School of Computer Science（计算机科学学院）
Pioneer Alpha（先锋阿尔法）；Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；University of Hertfordshire（赫特福德大学）；Westcliff university（韦斯特克利夫大学）；Clark Atlanta University（克拉克亚特兰大大学）
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
School of Engineering and Applied Sciences Washington State University（华盛顿州立大学工程与应用科学学院）；Data Sciences and Analytics Group Pacific Northwest National Laboratory（太平洋西北国家实验室数据科学与分析组）；Cognitive Engineering Center Georgia Institute of Technology（佐治亚理工学院认知工程中心）；Visual Analytics Group Pacific Northwest National Laboratory（太平洋西北国家实验室可视化分析组）
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model
Eindhoven University of Technology（埃因霍温理工大学）；Eindhoven Artificial Intelligence Systems Institute（埃因霍温人工智能系统研究所）；Ca’ Foscari University of Venice（威尼斯大学）
Exploring Trust Calibration in XAI - The Impact of Exposing Model Limitations to Lay Users
Research Center Trustworthy Data Science and Security（可信数据科学与安全研究中心）；University Alliance Ruhr（鲁尔大学联盟）；University of Duisburg-Essen（杜伊斯堡- Essen大学）；TU Dortmund University（多特蒙德技术大学）
Towards a Novel Measure of User Trust in XAI Systems
UGiVIA Research Group, University of the Balearic Islands, Dpt. of Mathematics and Computer Science（巴利阿里岛屿大学数学与计算机科学系UGiVIA研究组）；Hospital Universitari Son Espases（Son Espases大学医院）；SCOPIA Research Group, Department of Mathematical Sciences and Computer Science, University of the Balearic Islands（巴利阿里岛屿大学数学与计算机科学系SCOPIA研究组）；Computer Science, University of the Balearic Islands（巴利阿里岛屿大学计算机科学系）；Institute for Health Research of the Balearic Islands (IdISBa)（巴利阿里岛屿健康研究所）；Artificial Intelligence Research Institute of the Balearic Islands (IAIB), University of the Balearic Islands（巴利阿里岛屿人工智能研究所（IAIB），巴利阿里岛屿大学）
Uncertainty Awareness and Trust in Explainable AI- On Trust Calibration using Local and Global Explanations
Research Center Trustworthy Data Science and Security（可信数据科学与安全研究所以）；TU Dortmund University（图卢兹大学）；University Duisburg-Essen（杜伊斯堡- Essen大学）；University of Nottingham（诺丁汉大学）
A Comparative Approach to Explainable Artificial Intelligence Methods in Application to High-Dimensional Electronic Health Records: Examining the Usability of XAI
Scientific Explanations in Health Sciences: Causality, Trust, and Epistemic Adequacy
College of Mathematical Medicine, Zhejiang Normal University（浙江师范大学数学医学院）；DAIS, Ca’ Foscari University of Venice（威尼斯大学DAIS）；European Centre for Living Technology（欧洲生命技术中心）
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
School of Informatics, University of Edinburgh（爱丁堡大学信息学院）；Institute of Automotive Engineering, Technical University (TU) of Darmstadt（达姆施塔特工业大学汽车工程研究所）；School of Engineering and Physical Sciences, Heriot-Watt University（赫瑞瓦特大学工程与物理科学学院）
XAI-CF -- Examining the Role of Explainable Artificial Intelligence in Cyber Forensics
Department of Information Security, College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia（信息安全系，计算机科学与工程学院，哈伊尔大学，沙特阿拉伯）
Evolutionary Computation and Explainable AI: A Roadmap to Understandable Intelligent Systems
Queen’s University（女王大学）；Newcastle University（纽卡斯尔大学）；University of Stirling（斯特灵大学）；University of Parma（帕尔马大学）；Robert Gordon University（罗伯特戈登大学）；University of Trento（特伦托大学）；Universiteit Leiden（莱顿大学）；University of Exeter（埃克塞特大学）
