Explainable Artificial Intelligence Technology and Its Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2025 | Viewed by 47
Special Issue Editors
Interests: deep learning; explainable artificial intelligence (XAI); EEG analysis; brain–computer interface (BCI); seizure detection; iris recognition; FPGA-based deep learning hardware accelerator design; cognitive science
Interests: deep learning; explainable artificial intelligence (XAI); seizure detection; EEG analysis; brain–computer interface (BCI); FPGA-based deep learning hardware accelerator design
Special Issue Information
Dear Colleagues,
Explainable artificial intelligence (XAI) aims to address one of the most critical challenges in artificial intelligence: making AI systems transparent, interpretable and trustworthy for researchers, developers, and end-users. By elucidating the internal reasoning processes of AI models, XAI can enhance user trust and improve the reliability of automated decision-making systems.
This Special Issue welcomes the submission of high-quality original research and review articles exploring the application and emerging frontiers of XAI. In addition to the application of conventional XAI methods, we also encourage submissions exploring biologically inspired, cognitively motivated, and neuroscience-related XAI methods.
We invite studies covering theoretical foundations, algorithmic innovations, practical applications in various domains, and empirical evaluations of XAI methods. Relevant application areas include, but are not limited to, biomedical signal processing, computer vision, natural language processing, robotics, autonomous vehicles, recommender systems, trustworthy big data analytics, edge/IoT devices, finance, and cognitive sciences.
Topics of interest include, but are not limited to, the following:
- Domain‑specific applications of XAI in health care, autonomous systems, smart manufacturing, finance, cybersecurity, and environmental science;
- Cognitive science- and biologically inspired XAI methods;
- Hardware‑efficient and real‑time XAI on edge, mobile, FPGA, or neuromorphic platforms;
- Causal, counterfactual, and contrastive explanation frameworks;
- XAI for privacy preservation, fairness auditing, and responsible AI governance;
- Benchmarks, evaluation metrics, and open‑source toolkits for XAI;
- Human–AI interaction studies evaluating explanatory effectiveness.
Dr. Guoyang Liu
Prof. Dr. Weidong Zhou
Prof. Dr. Lan Tian
Guest Editors
Manuscript Submission Information
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Keywords
- explainable artificial intelligence (XAI)
- post hoc explainability
- ante‑hoc (intrinsic) interpretability
- causal and counterfactual explanations
- cognitive science‑inspired XAI
- biologically inspired XAI
- interpretable deep learning
- trustworthy and responsible AI
- human–AI interaction and usability studies
- explainable biomedical signal processing
- explainable computer vision
- explainable natural language processing
- edge and hardware‑efficient XAI
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