Universiteit Leiden

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Lecture | Seminar

Data Science and Leiden Law School

Friday 11 October 2019
Followed by drinks and snacks
Kamerlingh Onnes Building
Steenschuur 25
2311 ES Leiden
Lorentzzaal, A144

Register via this link.


The Data Science Research Programme's traveling seminar series along all faculties is visiting Leiden Law School! 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. We are just beginning to discover the great opportunities offered by this data science perspective for the innovation of legal research.

At this seminar the various appliances of Data Science at the Faculty of Law will be demonstrated in a series of short presentations by Bart Custers, Professor of Law and Data Science, and PhD candidates from the Data Science Research Programme. 

14.30 Doors open Coffee, tea
15.00 Joanne van der Leun Opening 
15.10 Gineke Wiggers Data Science and Legal Publications
15.30 Bart Custers Data Science and Big Data in Legal Research 
16.00 Break Coffee, tea
16.15 Manon Wintgens Patterns in the Tax Treaty System
16.35 Mark Pijnenburg  Data Science @ the Dutch tax office
17.05 Suzan Verberne Closing
17.10 Snacks and Drinks Global Lounge

Gineke Wiggers: Data Science and Legal Publications
Legal publications contain important legal information for legal professionals. But the documents, their metadata and the way they are used also provide interesting information for data science. This presentation focusses on citation information in legal publications. It compares citations in legal publications to other fields: do lawyers cite like other scholars, what are their reasons for citing, and is it true that there is no distinction between legal scholarly and practitioner orientated publications?

Bart Custers: Data Science and Big Data in Legal Research 
Data science and big data offer many opportunities for researchers, not only in the domain of data science and related sciences, but also for researchers in many other disciplines. The fact that data science and big data are playing an increasingly important role in so many research areas raises the question whether this also applies to the legal domain.
As will be shown in this presentation, data science and big data offer several methods and applications that may be also useful for the legal research and legal practice. Typical examples are quantitative legal predictions, accelerating legal research and improving regulation based on empirical legal research. Data scientists are currently analyzing large amounts of legal data, such as legislation, case law, journal articles, policy documents, etc. to find novel patterns and insights that may contribute to legal research and practice.

Manon Wintgens: Patterns in the Tax Treaty System
The international tax system consists of many parties, with many different roles and objectives. Due to its seemingly unpredictable behaviour, we hypothesize that this system can be modelled as a complex system. To investigate this, in this talk we consider and analyse the tax treaty system, which consists of tax treaties, i.e., agreements between jurisdictions that may change over time.
Our approach is based on pattern mining techniques, which can detect patterns in the changes of the tax treaties, thus revealing patterns in the behaviour of jurisdictions. Since existing pattern mining techniques cannot be directly applied to tax treaties data, we propose a specialised method that takes the specific properties of tax treaties into account. To avoid finding too many and irrelevant patterns, we use the minimum description length (MDL) principle to obtain a summary instead.
Our long-term goal is to gain new insights on all layers of the international complex system, i.e., 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.

Mark Pijnenburg: Data Science @ the Dutch tax office
In this contribution the focus is on data science practices at the Dutch tax office. At the start of the presentation the reasons are explained why the Dutch tax office invests in data science and analytics. This is followed by a practical example where risky tax returns are selected by a signal model that is trained on historical data. After this, attention will focus on actual safeguards that are in place to mitigate the risks that are introduced by data-driven decision making. Finally, some current legal dilemmas are discussed, such as transparency and limitations imposed by the law to actually support citizens.

The Data Science Research Programme is associated to LIACS and MI

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