PhD Position on Data Science Methods Detecting Legal Issues in Decentralized Systems

Decentralised technological infrastructures (e.g. blockchains, Decentralized Autonomous Organizations and Apps (DAOs, DApps)) promise a trustworthy technological environment for a plethora of societal and business applications. However, some features and the faults of their design create significant deviations from the societal expectations embodied in institutions, laws, and ethical frameworks, e.g., DAO malfunctions, breach of data protection or financial regulation, financial fraud, and the lack of accountability of infrastructure, service developers and operators. Those deviations are potential signs of incompatibility with the existing institutional, legal, economic, and social order, which may either hinder the innovation in this space, or if growth continues uninterrupted, may lead to societally undesirable consequences.

‘How to effectively detect and overcome legal compliance issues through the technical analysis of complex techno-social systems, including decentralized ones?’

This problem has emerged as an important research challenge for law and policy in general. On the one hand, new insights are needed into how these systems are designed and operated from the perspective of their creators (computer scientists), as well as the known and unknown societal risks they pose. On the other hand, legal scholars usually lack the necessary skills and expertise to conceptualise and study techno-social systems through empirical, quantitative methods. This limits the effectiveness of legal research in the information law and policy domain, despite the recent forceful turn towards evidence-based policymaking and empirical legal studies.

This PhD research will take steps towards creating a shared understanding, vocabulary, methodology at the intersection of law and data science.

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