Centralization enhances the efficiency of Artificial Intelligence (AI) but also introduces critical challenges, including single points of failure, inherent biases, data privacy risks, and scalability limitations. To address these issues, blockchain-based Decentralized Artificial Intelligence (DeAI) has emerged as a promising paradigm that leverages decentralization and transparency to improve the trustworthiness of AI systems. Despite rapid adoption in industry, the academic community lacks a systematic analysis of DeAI's technical foundations, opportunities, and challenges. This work presents the first Systematization of Knowledge (SoK) on DeAI, offering a formal definition, a taxonomy of existing solutions based on the AI lifecycle, and an in-depth investigation of the roles of blockchain in enabling secure and incentive-compatible collaboration. We further review security risks across the DeAI lifecycle and empirically evaluate representative mitigation techniques. Finally, we highlight open research challenges and future directions for advancing blockchain-based DeAI.

翻译：中心化提升了人工智能（AI）的效率，但也带来了关键挑战，包括单点故障、固有偏见、数据隐私风险以及可扩展性限制。为解决这些问题，基于区块链的去中心化人工智能（DeAI）作为一种有前景的范式应运而生，它利用去中心化和透明性来提高AI系统的可信度。尽管在工业界得到快速采用，学术界仍缺乏对DeAI技术基础、机遇与挑战的系统性分析。本文首次提出了关于DeAI的知识系统化（SoK），给出了正式定义，基于AI生命周期对现有解决方案进行了分类，并深入探讨了区块链在实现安全且激励相容的协作中所扮演的角色。我们进一步审视了DeAI生命周期中的安全风险，并对代表性的缓解技术进行了实证评估。最后，我们指出了推动基于区块链的DeAI发展的开放性研究挑战与未来方向。