Innovative Convergence of Blockchain and Explainable AI

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About this Research Topic

Submission deadlines

  1. Manuscript Submission Deadline 31 March 2025

  2. This Research Topic is still accepting articles.

Background

As the integration of Artificial Intelligence (AI) and Machine Learning (ML) technologies continues to permeate various aspects of our daily lives, it is becoming increasingly important to ensure that these AI models are not only effective in their operations but also transparent and interpretable by humans. This pressing need has given rise to the concept of Explainable AI (XAI), a field of research aimed at enhancing the transparency and interpretability of AI systems. The advent of XAI has come at a crucial time, as the future of communication networks, including the anticipated 6G and beyond, holds immense potential for transformative applications. These networks are expected to support an extensive array of real-time data analysis and decision-making tasks across diverse domains, ranging from healthcare and transportation to smart cities and industrial automation. However, the successful implementation of such applications relies heavily on the development of XAI solutions that can provide insights into the decision-making process of AI systems. By combining the power of XAI and advanced communication systems, a new ecosystem can emerge, one that leverages the rapid exchange of data across platforms to create social intelligence. The integration of XAI with future communication systems holds the potential to shape a more transparent, interpretable, and trustworthy AI-driven world, where humans can confidently rely on and benefit from the vast potential of AI and ML technologies. Moreover, blockchain technology, known for its decentralized and secure nature, offers significant opportunities to enhance the reliability and security of AI systems. The convergence of blockchain with XAI can lead to the creation of robust, transparent, and verifiable AI-driven applications. Blockchain's immutable ledger can provide a trustworthy foundation for recording AI decisions and data exchanges, further enhancing the transparency and accountability of AI systems.

This article collection aims to bring together the latest research and developments in the area of XAI, Blockchain and Explainable AI systems. The scope of the collection includes, but is not limited to, the following topics:

• Blockchain and XAI Integration systems
• XAI techniques for future communication networks
• XAI applications in network optimization and management
• XAI approaches for data analytics and decision-making in communication networks
• Ethics and social implications of XAI in secure communication networks
• Real-time data analysis and decision-making solutions
• Case studies and applications of Blockchain in communication networks
• Challenges and opportunities in integrating XAI into communication networks
• Ethics and social implications of XAI in communication networks
• Real-time data analysis and decision-making solutions
• Secure and trust XAI Models for 6G and beyond systems
• Security and privacy issues in federated learning
• Real-time data analysis and decision-making solutions

We invite original, high-quality contributions that address the above topics. Manuscripts can be submitted as full-length articles, short communications, or review articles. All submissions will be subject to a rigorous peer-review process to ensure high-quality publications. Manuscripts should be prepared according to the journal's guidelines.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

  • Community Case Study
  • Data Report
  • Editorial
  • Hypothesis and Theory
  • Methods
  • Mini Review
  • Opinion
  • Original Research
  • Perspective

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Keywords: Blockchain, Explainable AI, Machine Learning, Artificial Intelligence

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