Digital Enforceable Contracts (DEC): Making Smart Contracts Smarter

Abstract

The combination of smart contracts with blockchain technology enables the authentication of the contract and limits the risks of non-compliance. In principle, smart contracts can be processed more efficiently compared to traditional paper-based contracts. However, current smart contracts have very limited capabilities with respect to normative representations, making them too distant from actual contracts. In order to reduce this gap, the paper presents an architectural analysis to see the role of computational artifacts in terms of various ex-ante and ex-post enforcement mechanisms. The proposed framework is assessed using scenarios concerning data-sharing operations bound by legal requirements from the General Data Protection Regulation (GDPR) and data-sharing agreements.

Publication
Frontiers in Artificial Intelligence and Applications
Lu-Chi Liu
Lu-Chi Liu
PhD Candidate

I am a PhD student in the Complex Cyber Infrastructure (CCI) group. I come from Taiwan and have my master’s degree in Computer Science at National Taiwan University. After that, I worked in the software industry as a DevOps engineer for about two years and decided to persue a doctoral degree abroad. Being supervised by Tom van Engers and L.Thomas van Binsbergen, my research focuses on the implementation of digital enforceable contracts, investigating blockchain, smart contracts, compliance, governance, normative systems, adversarial settings, etc. I will be working on the SSPDDP project which aims to create secure, scalable and policy-enforced environment for data exchange. Apart from this, I like to play volleyball, watch Netflix series, taste delicious food and attend various activities in my free time!

Giovanni Sileno
Giovanni Sileno
Assistant Professor
Tom van Engers
Tom van Engers
Full Professor (FDR)

I conduct research on AI & Law, with a particular focus on normative reasoning. Having a track record in AI & Law research going back to 1983, I have worked both on knowledge-driven as well as data-driven AI approaches.

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