Blockchain and Explainable AI: Pioneering Transparency and Trust in Future Communication Networks

The intersection of Artificial Intelligence (AI) and Machine Learning (ML) with blockchain technology represents a revolutionary frontier within scientific inquiry, particularly when applied to the domain of Explainable AI (XAI). As AI and ML technologies become increasingly integrated into sectors like healthcare, transportation, and smart cities, the need for transparency and reliability grows more urgent. Explainable AI emerges as a vital field dedicated to making AI's complex decision-making processes understandable to humans. This transparency becomes even more critical as we anticipate the deployment of 6G communication networks, poised to handle vast amounts of real-time data analysis and decision-making tasks. These developments necessitate robust XAI solutions that elucidate AI operations, fostering trust and confidence in AI-driven systems. Despite their promise, challenges persist in creating a seamless interface between XAI and emerging technologies where blockchain could cement its role, enhancing system reliability and security through its decentralized, immutable nature.

This Research Topic aims to explore the convergence of blockchain technology with Explainable AI to develop secure and credible AI applications. By integrating blockchain’s secure ledger capabilities, we aspire to improve the accountability and verification of AI systems, ensuring transparency in decision-making processes. Our primary objective is to delve into how blockchain technology can underpin the interpretability of future AI communication systems by offering innovative ways to log and audit AI outputs. The research will focus on blending XAI's interpretability with blockchain’s decentralized assurance, proposing a new era of data integrity and security, paralleling the launch of next-generation communication infrastructures.

To gain deeper insights into this pioneering field, we welcome articles investigating the intersection of blockchain with Explainable AI across various settings, particularly within communication networks. Topics of interest include:

- Blockchain and XAI integration systems

- XAI tools for optimizing future communication networks

- Applications of XAI in network optimization and management

- Ethical and social ramifications of deploying XAI in secure networks

- Real-time data solutions leveraging blockchain for communication

- Case studies highlighting blockchain applications in network settings

- Security and privacy challenges in federated learning environments

- Innovative XAI models designed for emerging 6G infrastructures

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
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• ... View all formats

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

Article types

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

• Community Case Study
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Policy and Practice Reviews
• Policy Brief
• Review
• Systematic Review
• Technology and Code

Keywords: Blockchain technology, Explainable AI (XAI), Transparency, 6G communication networks, Machine learning, Data integrity, Decentralized systems, Ethical AI, Network optimization, Data security

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.