Recent Developments in Explainable Artificial Intelligence
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 368
Special Issue Editors
Interests: trustworthy AI; AI model stealing; XAI; applications in cybersecurity
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
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
Manuscript Submission Information
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Keywords
- interpretable machine learning
- explanation generation
- local/global/multimodal/counterfactual explanations
- human-in-the-loop
- explanation validation
