Explainable Machine Learning and Computer Vision
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 30 January 2026 | Viewed by 3
Special Issue Editors
Interests: artificial intelligence; computer vision; signal processing; machine learning; signal analysis; object detection; uav
Interests: artificial intelligence; digital signal processing; image analysis; intelligent systems
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; multimodal data/signals; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Explainable machine learning and computer vision are essential for building transparent, trustworthy artificial intelligence that can be used in critical domains, such as medical imaging, autonomous driving, and industrial inspection, where understanding the model’s decisions is key for safety, fairness, and compliance.
The goal of this Special Issue is to compile papers on the methods, evaluations, and applications that make machine learning and computer vision-based artificial intelligence systems interpretable and accountable. Topics relevant to this Special Issue include those listed in the keywords below:
Saliency and feature attribution;
Counterfactual and concept explanation methods;
Model-agnostic approaches;
Metrics and benchmarks for explainability;
Human-centered evaluation of interpretability;
Explainability in video, 3D vision, and multimodal tasks;
Integration of explainable AI into visualization dashboards and reporting.
Dr. Vytautas Abromavičius
Prof. Dr. Artūras Serackis
Prof. Dr. Dalius Matuzevičius
Guest Editors
Manuscript Submission Information
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Keywords
- saliency and feature attribution
- counterfactual and concept explanation methods
- model-agnostic approaches
- metrics and benchmarks for explainability
- human-centered evaluation of interpretability
- explainability in video, 3D vision, and multimodal tasks
- integration of explainable AI into visualization dashboards and reporting
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