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A computational framework is proposed that generates predictions regarding future litigation events related to EU law. This will enable policy-makers from member states to foresee issues on policy implementations resulting from releases of mandatory EU legislation. For example, transpositions often require adaptations to national legal systems, and it is crucial that any delays or problems regarding provisions implementation are minimized so that all citizens can fully benefit from EU laws. Reliable data-driven aid provides decision-makers with the opportunity to perform early social, economic and environmental assessments of legal initiatives and enable governments to be proactive and transparent on policy-making life-cycle processes.
Machine Learning, Topological Features, EU law, Policymaking, Complex Networks
Machine Learning, Topological Features, EU law, Policymaking, Complex Networks
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