Recent Developments in Explainable Artificial Intelligence

Dear Colleagues,

Artificial Intelligence (AI) has rapidly advanced in recent years, bringing significant breakthroughs in areas such as natural language processing, computer vision, and robotics, among many others. However, as AI systems become more complex, they become less transparent, and their decision-making processes become harder to interpret. This lack of interpretability poses a significant challenge for AI adoption in critical applications, where the ability to explain decisions and actions is essential. Explainable AI (XAI) aims to address this challenge by developing techniques that can provide clear, understandable, and trustworthy explanations for the behavior and outputs of AI systems.

This Special Issue aims to bring together the latest research on recent developments in XAI. We invite researchers and practitioners to submit original articles on topics including, but not limited to, the following topics:

• Novel techniques for generating explanations from AI models;
• New AI models with improved interpretability;
• Evaluation methodologies for XAI;
• Real-world applications of XAI;
• Human factors and user-centered design for XAI;
• Theoretical foundations of XAI.

Dr. Sangkyun Lee
Prof. Dr. Kyu-Baek Hwang
Guest Editors

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