Trustworthy AI for Video, Web, and Social Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".
Deadline for manuscript submissions: 15 January 2026 | Viewed by 82
Special Issue Editors
Interests: action recognition; anomaly detection; video image processing; one- and few-shot learning; deep learning; tensor learning; domain adaptation
Special Issues, Collections and Topics in MDPI journals
Interests: bioinformatics; computer vision; machine learning; human-centered computing; biological computing
Interests: artificial intelligence; computer vision; pattern recognition; big-data analysis; biometrics; medical imaging; Internet of Things (IoT); image processing; biomedical engineering
Special Issues, Collections and Topics in MDPI journals
Interests: responsive AI; neural/deep learning; responsible AI; human-centered AI; affective computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This proposed Special Issue seeks to explore the frontier of trustworthy, cross-domain, and human-centric AI solutions—particularly at the intersection of video processing, social data, and web technologies. This Special Issue builds upon the successful TIME 2025 workshop (https://time.griffith.edu.au/workshop/time2025/) at WWW 2025, which underscored the urgency of addressing challenges such as misinformation, privacy, ethical AI, and algorithmic transparency across diverse digital environments.
(1) Scope, Focus, and Purpose
Focus:
The focus of this Special Issue is to investigate innovative computational methods that bridge technical advancement with societal impact, particularly in video analytics, social computing, and web AI.
Scope:
This Special Issue welcomes high-quality original research papers, extended workshop papers, and comprehensive reviews on the following topics:
- Interpretable and robust video analytics;
- Secure and ethical AI in clinical and web domains;
- Privacy-preserving machine learning;
- Cross-lingual and cross-domain model generalization;
- Human motion understanding and smart city applications;
- Fair and explainable content moderation;
- Multimodal misinformation detection and mitigation.
Purpose:
The purpose of this Special Issue is to inspire a new wave of responsible, cross-disciplinary AI research that is deeply aligned with both practical utility and ethical mandates, ensuring broader adoption and trust in real-world systems.
(2) Relation to Existing Literature
While literature exists in silos—be it action recognition, content moderation, or healthcare AI—few collections holistically address how methods from one domain (e.g., federated learning in health) can inform others (e.g., privacy in social media). This Special Issue uniquely positions itself to foster methodological synthesis and real-world adaptation, directly supplementing recent WWW, NeurIPS, and AAAI contributions.
We look forward to your contributions.
Dr. Lei Wang
Dr. Md Zakir Hossain
Dr. Syed Mohammed Shamsul Islam
Prof. Dr. Tom Gedeon
Guest Editors
Manuscript Submission Information
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Keywords
- trustworthy AI
- video understanding
- cross-domain learning
- privacy and fairness
- responsible machine learning
- content moderation
- multimodal misinformation detection
- human-centered AI
- ethical web technologies
- interpretable deep learning
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