Explainable Machine Learning and Computer Vision

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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