Future Development of Trustworthy Artificial Intelligence and Applications
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 249
Special Issue Editors
Interests: trustworthy AI; privacy, robustness, and security in machine learning; decentralized machine learning; IoT and edge computing; foundation models; reinforcement learning
Interests: AI; ML; optimization; computer vision; natural language processing; data science; with two major focuses on (A) establishing robust and efficient AI systems; (B) bridging the gap between AI and societal & scientific challenges
Interests: machine learning (ML); artificial intelligence (AI) in the Internet of Things (IoT); cyber-physical systems (CPS); location-based services (LBS); edge computing; remote sensing imagery analysis
Special Issue Information
Dear Colleagues,
As AI transforms our lives, it is essential to ensure its accountability, security, and transparency for improving public trust and promoting scalable AI adoption. This Special Issue responds to the increasing need for innovative research and applications that advance trustworthy AI systems. We aim to foster a research community to drive the development of trustworthy AI that reflects social values, needs, and expectations. By empowering individuals to take an active role in shaping trustworthy AI systems, we can ensure a future where AI is developed with responsibility and trust in mind.
This Special Issue solicits both fundamental and applicational research that presents the latest advancements and challenges in the development of trustworthy AI. We welcome research that explores the technical and social dimensions of trustworthy AI, investigates the status quo vulnerabilities of AI systems, develops principled countermeasures to interpret AI decision-making and enhance AI accountability, and promotes the deployment of trustworthy AI in diverse fields.
We invite submissions from researchers at various levels of their careers. Specific topics include the following:
- Vulnerability analysis in AI systems.
- Interpretable AI decision-making.
- Fairness and bias mitigation for AI systems.
- Privacy-preserving AI.
- AI ethics and value alignment.
- Human–AI interaction with trust and reliability.
- Hardware and algorithm co-design for robust AI.
- AI accountability through benchmarking.
- Trustworthy AI applications for realistic and high-stake fields.
Dr. Zhuangdi Zhu
Dr. Tianlong Chen
Dr. Fei Dou
Guest Editors
Manuscript Submission Information
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Keywords
- AI robustness
- security
- fairness
- interpretability
- hardware–algorithm codesign
- value alignment
- human–AI interaction
- application and benchmarking
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