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Blockchain Oracles: Implications for Smart Contracts in Legal Reasoning and Addressing the Oracle Problem

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Published:07 December 2023Publication History

ABSTRACT

This paper presents a comprehensive investigation into the role, functionalities, and complexities of blockchain oracles, focusing particularly on the implications for smart contracts in legal reasoning contexts. Oracles serve as a vital bridge to smart contracts’ inability to interact with external or “off-chain” data, enabling them to be used in a variety of real-world situations. Oracle’s integration, however, introduces a number of complexities, including security vulnerabilities, collectively referred to as the Oracle Problem. In addition to a review of existing literature, we also provide a mathematical analysis quantifying the computational complexity associated with automating legal reasoning and a novel design framework aimed at establishing oracles that are secure, efficient, and legally compliant. The paper aims to serve as a foundational text for researchers, legal practitioners, and blockchain developers, advancing the academic discourse surrounding blockchain oracles and their role in smart contracts.

References

  1. Jean Bacon, Johan David Michels, Christopher Millard, and Jatinder Singh. 2017. Blockchain Demystified. (12 2017). https://ssrn.com/abstract=3091218Google ScholarGoogle Scholar
  2. Hillel Bavli. 2006. Applying the Laws of Logic to the Logic of Laws Applying the Laws of Logic to the Logic of Laws APPLYING THE LAWS OF LOGIC TO THE LOGIC OF LAW. https://scholar.smu.edu/cgi/viewcontent.cgi?article=1043&context=law_facultyGoogle ScholarGoogle Scholar
  3. Abdeljalil Beniiche. 2020. A Study of Blockchain Oracles. arXiv:2004.07140 [cs] (07 2020). https://arxiv.org/abs/2004.07140Google ScholarGoogle Scholar
  4. Christopher Bishop. 2006. Pattern recognition and machine learning. Springer Verlag.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Gerrit Bleumer. 2005. Random Oracle Model. 514–515. https://doi.org/10.1007/0-387-23483-7_343Google ScholarGoogle ScholarCross RefCross Ref
  6. Lorenz Breidenbach, Christian Cachin, Benedict Chan, Alex Coventry, Steve Ellis, Ari Juels, Farinaz Koushanfar, Andrew Miller, Brendan Magauran, Daniel Moroz, Sergey Nazarov, Alexandru Topliceanu, and Florian Tramèr. 2021. Chainlink 2.0: Next Steps in the Evolution of Decentralized Oracle Networks. https://research.chain.link/whitepaper-v2.pdfGoogle ScholarGoogle Scholar
  7. Ioannis Caragiannis and Nikolaj I. Schwartzbach. 2022. Adjudication with Rational Jurors. CoRR abs/2201.06597 (2022). arXiv:2201.06597https://arxiv.org/abs/2201.06597Google ScholarGoogle Scholar
  8. Antonio R Damasio. 1994. Descartes error : emotion, reason and the human brain. Vintage Books. 247–248 pages.Google ScholarGoogle Scholar
  9. Alexander Egberts. 2017. The Oracle Problem - An Analysis of how Blockchain Oracles Undermine the Advantages of Decentralized Ledger Systems. SSRN Electronic Journal (2017). https://doi.org/10.2139/ssrn.3382343Google ScholarGoogle ScholarCross RefCross Ref
  10. Chainlink Foundation. 2020. The 3 Levels of Data Aggregation in Chainlink Price Feeds. https://blog.chain.link/levels-of-data-aggregation-in-chainlink-price-feedsGoogle ScholarGoogle Scholar
  11. Naman Goel, Cyril van Schreven, Aris Filos-Ratsikas, and Boi Faltings. 2020. Infochain: A Decentralized, Trustless and Transparent Oracle on Blockchain. Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence (07 2020), 4604–4610. https://doi.org/10.24963/ijcai.2020/635Google ScholarGoogle ScholarCross RefCross Ref
  12. Everett Hildenbrandt, Manasvi Saxena, Nishant Rodrigues, Xiaoran Zhu, Philip Daian, Dwight Guth, Brandon Moore, Daejun Park, Yi Zhang, Andrei Stefanescu, and Grigore Rosu. 2018. KEVM: A Complete Formal Semantics of the Ethereum Virtual Machine., 204–217 pages. https://doi.org/10.1109/CSF.2018.00022Google ScholarGoogle ScholarCross RefCross Ref
  13. Mudabbir Kaleem and Weidong Shi. 2021. Demystifying Pythia: A Survey of ChainLink Oracles Usage on Ethereum. Lecture Notes in Computer Science (2021), 115–123. https://doi.org/10.1007/978-3-662-63958-0_10Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. H. Li and T. Xie. 2021. A Logistics Proof Chain for the Decentralized Trading Model Based on Two-Tier Identities. In 2021 2nd International Conference on Computer Science and Management Technology (ICCSMT). IEEE Computer Society, Los Alamitos, CA, USA, 142–146. https://doi.org/10.1109/ICCSMT54525.2021.00036Google ScholarGoogle ScholarCross RefCross Ref
  15. Sheldon Mark D.2021. Preparing Auditors for the Blockchain Oracle Problem. Current Issues in Auditing 15 (5 2021). Issue 2. https://doi.org/10.2308/ciia-2021-007Google ScholarGoogle ScholarCross RefCross Ref
  16. John Middleton. 2017. Statutory Interpretation: Mostly Common Sense?, 626-656 pages. https://law.unimelb.edu.au/__data/assets/pdf_file/0007/2369590/06-Middleton-402-Post-Press.pdfGoogle ScholarGoogle Scholar
  17. Marta Poblet, Darcy W. E. Allen, Oleksii Konashevych, Aaron M. Lane, and Carlos Andres Diaz Valdivia. 2020. From Athens to the Blockchain: Oracles for Digital Democracy. Frontiers in Blockchain 3 (09 2020). https://doi.org/10.3389/fbloc.2020.575662Google ScholarGoogle ScholarCross RefCross Ref
  18. Tania Sourdin. 2018. Judge v Robot? Artificial Intelligence and Judicial Decision-Making. University of New South Wales Law Journal 41 (11 2018). https://doi.org/10.53637/zgux2213Google ScholarGoogle ScholarCross RefCross Ref
  19. Symposium on Second-Best Theory and Law & Economics [n. d.]. Machine Intelligence and Legal Reasoning - The Charles Green Lecture in Law and Technology. Vol. 73. Symposium on Second-Best Theory and Law & Economics, Chicago-Kent Law Review.Google ScholarGoogle Scholar
  20. Anne Von. 1987. An artificial intelligence approach to legal reasoning. Mit Press. 24–26 pages.Google ScholarGoogle Scholar
  21. Liuwen Yu, Mirko Zichichi, Réka Markovich, and Amro Najjar. 2022. Intelligent Human-input-based Blockchain Oracle (IHiBO). Proceedings of the 14th International Conference on Agents and Artificial Intelligence 1 (2022). https://doi.org/10.5220/0010945300003116Google ScholarGoogle ScholarCross RefCross Ref

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        • Published in

          cover image ACM Other conferences
          SOICT '23: Proceedings of the 12th International Symposium on Information and Communication Technology
          December 2023
          1058 pages
          ISBN:9798400708916
          DOI:10.1145/3628797

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          Publication History

          • Published: 7 December 2023

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