Data Science Research Programme
The Faculty of Law
Innovative data science methodologies offer new and exciting possibilities to study the legal system’s behaviour, gain new possibilities to enhance the quality of legal decision-making and gives new insights into the complex interaction between legal actors.
Amongst others, the legal system can be observed as an enormous information processing system. The design of laws and regulations, court procedures and governmental enforcement activities are all based on and produce data and data products. Data processing and analysis techniques have been supporting legal practice and the execution of laws and regulations by governmental agencies for a long time.
Driven by current ICT innovation, data science techniques and experts in swift pace become the core of legal value and knowledge creation processes. Intelligent processing of large amounts of legal documents enhances the quality of legal advice and judgments. Machine learning based applications for instance, are already being used to predict court decisions, and governmental organisations like tax administrations enrol data science departments in order to improve their inspection’s hit rates.
Building computational models of (parts and aspects of) the legal system supports new ways of scientific legal research as well. New questions can be formulated and new and surprising insights gained. This scientific approach will focus on the system level rather than a case level. Examples of this system’s perspective are the measurement and assessment of the impact of legal decisions and scholarly comments, the interaction between legal systems over time, or the design and analysis of the behaviour of (actors within) legal systems from a network perspective.
We are just beginning to discover the great opportunities offered by this data science perspective for the innovation of legal research.
Data Science Research Projects
Measuring relevance and relations of Dutch legal publications
Legal scholars and professionals are confronted with a rapidly increasing volume of legal publications. Only part of these publications are relevant enough to be cited. This project aims to determine which documents that are, and whether alternative metrics are a reliable way to predict whether documents will be cited, in order to be able to present the user the most relevant publications first.
The international tax system as a complex system
The international tax system is composed of multiple layers, i.e., law and regulations, jurisdictions, and businesses. Previously, these inherently different layers were often analysed from a fiscal perspective. In contrast, this data-driven research project aims to study the international tax system in its entirety from a complex systems perspective.
Complex systems can be characterized as systems in which multiple components interact with each other, often in non-linear ways. The main goal of this research will be to investigate if and how the international tax system can be defined and modelled as a complex system. Approaching the international tax system from this perspective aims to gain new insights on all layers, e.g., 1) on the effect of new and modified tax treaties; 2) on the interaction between jurisdictions; and 3) on the behaviour of business strategies over time. In addition, the project will address questions related to the existence of, e.g., tax gaps, legislative patterns, and tax havens and how these are reflected in observational data.
The project takes an innovative approach that combines data science with system modelling on data sources including tax treaties and financial information of businesses. By applying, e.g., network modelling and pattern discovery, the researchers aim to understand the behaviour of the international tax system as a complex system.