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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• mathematical modeling
• computer vision
• optimization methods
• probabilistic inference
• explainable AI
• formal verification
• trustworthy machine learning
• variational methods
• geometry in deep learning