Explainable and Interpretable Computer Vision: Towards Trustworthy and Transparent AI
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 May 2026 | Viewed by 16
Special Issue Editors
Interests: computer vision; generative AI
Special Issue Information
Dear Colleagues,
Computer vision models, particularly those based on deep learning, are achieving unprecedented performance across a wide range of applications, from medical diagnosis to autonomous driving. However, the complexity of these models often renders them opaque, creating a "black box" problem where their decisions are difficult for humans to understand. This lack of transparency presents a significant barrier to their adoption in high-stakes domains where trust, accountability, and reliability are paramount. The ability to explain a model's reasoning is crucial for debugging errors, identifying biases, and building confidence in AI systems.
The purpose of this Special Issue is to present cutting-edge research on the methodologies and frameworks that make computer vision models more transparent and interpretable. We aim to move beyond theoretical discussions to explore practical solutions that can be applied to real-world problems. The scope of this Special Issue is broad, inviting papers on topics including, but not limited to, the following:
- Attribution-based methods: Techniques that explain model predictions by quantifying the contribution of each input feature to the final output.
- Prototype methods: Methods to select representative examples from the training data, called prototypes, to explain the model's predictions by showing which prototypes are most similar to a given input.
- Structured explanations: Algorithms about rule-based or hierarchical explanations, systematically capturing both local saliency and global reasoning pathways of underlying models.
- Human-in-the-Loop Systems: Research on how explainable systems can improve human–AI collaboration for decision support and analysis.
- Applications: Case studies of XAI in critical domains such as healthcare, security, surveillance, and autonomous vehicles.
Dr. Jun Li
Dr. Fang Kong
Guest Editors
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Keywords
- attribution methods
- saliency maps
- counterfactual explanations
- prototype methods
- structured explanations
- shapley values
- human-in-the-loop ai
- ai in healthcare
- autonomous driving
- decision support systems
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