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Machine Learning for Blockchain: Literature Review and Open Research Questions

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  • Zhang, Luyao

Abstract

In this research, we explore the nexus between artificial intelligence (AI) and blockchain, two paramount forces steering the contemporary digital era. AI, replicating human cognitive functions, encompasses capabilities from visual discernment to complex decision-making, with significant applicability in sectors such as healthcare and finance. Its influence during the web2 epoch not only enhanced the prowess of user-oriented platforms but also prompted debates on centralization. Conversely, blockchain provides a foundational structure advocating for decentralized and transparent transactional archiving. Yet, the foundational principle of "code is law" in blockchain underscores an imperative need for the fluid adaptability that AI brings. Our analysis methodically navigates the corpus of literature on the fusion of blockchain with machine learning, emphasizing AI's potential to elevate blockchain's utility. Additionally, we chart prospective research trajectories, weaving together blockchain and machine learning in niche domains like causal machine learning, reinforcement mechanism design, and cooperative AI. These intersections aim to cultivate interdisciplinary pursuits in AI for Science, catering to a broad spectrum of stakeholders.

Suggested Citation

  • Zhang, Luyao, 2023. "Machine Learning for Blockchain: Literature Review and Open Research Questions," OSF Preprints g2q5t, Center for Open Science.
    • Handle: RePEc:osf:osfxxx:g2q5t
    DOI: 10.31219/osf.io/g2q5t
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    References listed on IDEAS

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    1. Will Cong & Campbell Harvey & Daniel Rabetti & Zong-Yu Wu, 2025. "An Anatomy of Crypto-Enabled Cybercrimes," Management Science, INFORMS, vol. 71(4), pages 3622-3633, April.
    2. Ekaterina Zhuravskaya & Maria Petrova & Ruben Enikolopov, 2020. "Political Effects of the Internet and Social Media," Annual Review of Economics, Annual Reviews, vol. 12(1), pages 415-438, August.
    3. Luyao Zhang & Fan Zhang, 2023. "Understand Waiting Time in Transaction Fee Mechanism: An Interdisciplinary Perspective," Papers 2305.02552, arXiv.org.
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    Cited by:

    1. Jingfeng Chen & Wanlin Deng & Dangxing Chen & Luyao Zhang, 2024. "FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial Machine Learning," Papers 2411.16277, arXiv.org.

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