Human-Centric Explainability in Large-Scale IoT and AI Systems

Dear Colleagues,

Since the expansion of artificial intelligence (AI) and the Internet of Things (IoT) is accelerating, the widespread adoption of transparent, interpretive, and human-centered solutions in large-scale systems is required. With such complex systems consisting of billions of connected devices, Explainable AI (XAI) must not only deliver accurate decisions but also engender user trust and understanding.

This Special Issue will enable maximum explainability integration into AI-driven IoT systems at scale. Specific topics of interest include constructing new XAI approaches to manage large-scale IoT architectures, increasing transparency in real-time decision-making, and human-centric ways to interpret IoT data. We particularly welcome contributions that discuss scaling issues, ethical issues, and regulatory issues affecting XAI for IoT ecosystems.

In addition, this Special Issue covers research on the use of XAI in domains such as smart cities, healthcare, industrial IoT, and environmental monitoring, where there are tradeoffs between interpretability and performance. Through fostering interdisciplinary collaboration, this Special Issue will pave the way for next-generation IoT systems that are both intelligent and trusted and user-centric.

We invite original research, review articles, and case studies on emerging XAI approaches where the solutions are found at the intersection of XAI, large-scale systems, sensor networks, and the IoT.

Topics of interest include, but are not limited to, the following:

• Explainable AI for distributed and federated IoT systems;
• Human-centric XAI interfaces for IoT applications;
• XAI for real-time decision-making in IoT;
• Integration of explainable AI with edge and fog computing in IoT;
• Ethical and regulatory challenges of XAI in IoT;
• Explainable AI for cross-domain IoT interoperability;
• Trustworthiness and transparency in IoT cybersecurity;
• AI-driven IoT insights for smart cities;
• Next-generation explainable AI for autonomous IoT systems;
• IoT Data Engineering;
• IoT Data Management;
• Federated Learning for IoT;
• Ethical AI in IoT Systems.

Dr. Aristeidis Karras
Dr. Massimo Cafaro
Guest Editors

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