International Workshop on The Interplay Between Blockchain and Artificial Intelligence (AIBC 2025)

The convergence of blockchain technology and artificial intelligence (AI) has emerged as a transformative force across industries, redefining trust, transparency, and autonomy in digital ecosystems. This workshop aims to explore the symbiotic relationship between blockchain and AI, with a focus on cutting-edge advancements in large language models (LLMs) and their integration with Web 3.0 frameworks. By addressing theoretical foundations, practical applications, and ethical implications, this issue will provide a comprehensive roadmap for researchers and practitioners navigating this interdisciplinary frontier.

The rapid evolution of AI, particularly large-scale generative models like ChatGPT and DeepSeek, has unlocked unprecedented capabilities in data processing, automation, and decision-making. Simultaneously, blockchain technologies have matured to support decentralized governance, tokenized economies, and secure data sharing. Web 3.0, envisioned as a user-centric, decentralized internet, further bridges these domains by emphasizing interoperability, privacy, and user sovereignty.

This workshop seeks to address critical questions: How can blockchain enhance the transparency, security, and ethical governance of AI systems? What role do large models play in advancing decentralized applications (dApps) and Web 3.0 ecosystems? How might decentralized AI frameworks mitigate risks like data monopolies and algorithmic bias?

This workshop invites original research, case studies, and reviews at the intersection of blockchain, AI, large models, and Web 3.0. Key themes include (but are not limited to):

• Decentralized AI and Large Models

  - Federated learning and blockchain for privacy-preserving AI training.

  - Decentralized compute markets for large model inference (e.g., Bittensor, Gensyn).

  - Tokenization of AI services and model ownership (e.g., NFTs for generative AI outputs).

  - Governance mechanisms for open-source LLMs in Web 3.0 environments.

• Blockchain for AI Trust and Security

  - Auditable AI: Blockchain-based provenance tracking for training data and model behavior.

  - Smart contracts for automated AI governance and compliance.

  - Mitigating adversarial attacks on AI systems via decentralized consensus.

• Web 3.0 and AI-Driven Ecosystems

  - AI-powered DAOs (Decentralized Autonomous Organizations) for decentralized decision-making.

  - Semantic Web 3.0: Integrating LLMs with decentralized knowledge graphs.

  - Decentralized identity systems enhanced by AI (e.g., Sybil-resistant reputation models).

• Ethical and Regulatory Challenges

  - Bias detection and fairness in blockchain-augmented AI systems.

  - Legal frameworks for AI-blockchain hybrids in GDPR and Web 3.0 contexts.

  - Environmental sustainability of energy-intensive AI/blockchain workflows.

• Industry Applications

  - DeFi: AI-driven risk assessment and blockchain-based credit scoring.

  - Healthcare: Secure sharing of medical data via blockchain with AI diagnostics.

  - Smart Cities: Decentralized AI for IoT coordination and energy optimization.

Submission Guidelines

All submissions must be in English and in PDF format. Submissions that do not comply with the above instructions will be desk rejected without review. Please use the Springer proceeding template. Workshop papers should be 12-16 pages. All accepted papers will be published by Springer and will be indexed by EI Compendex.

Submissions to the AIBC 2025 workshop that meet the above requirements can be made via the submission site EasyChair by the submission deadline.

Distinguished papers presented at the conference, after further revision, will be published in the journal of Blockchain: Research and Applications. (Impact Factor 6.9, CiteScore 11.3)

Important Dates

• Full paper submissions: June 09, 2025 (GMT+8)
• Notification to authors: July 04, 2025 (GMT+8)

Organization Chairs

• Dr. Shan Jiang, Sun Yat-sen University, Zhuhai, China
• Dr. Jiaxing Shen, Lingnan University, Hong Kong SAR, China
• Dr. Chengzu Dong, Lingnan University, Hong Kong SAR, China