Mathematical Foundations and Approaches to AI Governance in Computer Vision

Dear Colleagues,

AI Governance in Computer Vision has emerged as a critical and rapidly evolving area of interdisciplinary research. This field sits at the intersection of artificial intelligence, visual computing, and mathematical sciences, requiring a deep integration of mathematical principles such as optimization, statistics, variational methods, algebraic structures, differential geometry, and information theory. These mathematical foundations not only support the design of robust and interpretable vision algorithms but are also vital for embedding ethical, transparent, and accountable governance mechanisms into computer vision systems.

In contemporary computer vision applications—such as image understanding, object recognition, and 3D reconstruction—governance concerns are increasingly prominent. Issues like algorithmic bias, lack of interpretability, vulnerability to adversarial inputs, and misalignment with societal values demand rigorous mathematical treatment. To ensure fairness, explainability, robustness, and policy compliance, AI governance in computer vision relies heavily on tools such as formal verification, probabilistic modeling, risk quantification, and causal inference.

This Special Issue aims to provide a focused platform for researchers to present original and high-quality contributions on the mathematical foundations and methods that enable trustworthy, governable computer vision systems. We particularly encourage submissions that bridge theoretical innovation with real-world applications and promote interdisciplinary collaboration across mathematics, computer science, and the broader AI ethics and governance communities.

Topics include but are not limited to:

• Mathematical modeling for image analysis and 3D vision
• Variational methods and PDEs in computer vision
• Optimization and numerical algorithms for vision tasks
• Statistical and probabilistic methods in visual learning
• Algebraic and geometric methods in deep learning
• Mathematical approaches to AI fairness and accountability
• Formal verification and logic-based AI system analysis
• Trustworthy AI: risk quantification, uncertainty modeling, and robustness
• Interpretable machine learning and causal inference
• Mathematical ethics and quantitative governance frameworks

We look forward to receiving your contributions.

Prof. Dr. Jian Zhao
Dr. Lei Jin
Dr. Zhaoxin Fan
Dr. Yalan Qin
Guest Editors

Dr. Xiaoguang Tu
Dr. Chen Min
Guest Editors Assistants

Manuscript Submission Information

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