UM Data Science Research Seminar with Law & Tech Lab
The UM Data Science Research Seminar Series are monthly sessions organized by the Institute of Data Science, in collaboration with different departments across UM. The aim of these sessions is to bring together scientists from all over Maastricht University to discuss breakthroughs and research topics related to Data Science. This seminar is organized in collaboration with the Law & Tech Lab.
All events are in-person and free of charge. We also offer participants a FREE lunch.
Schedule
LECTURE 1
Time: 12:00 - 12:30
Speaker: Antoine Louis
Title: AI for Law: Bridging the Gap between People and the Law with Language Models
Abstract: While becoming increasingly available online, the law remains challenging to search, investigate, and understand from an average citizen's perspective. This barrier to accessing legal information creates a clear imbalance within the legal system, preventing the right to equal access to justice for all. Our research project aims to investigate the use of neural language models to address the problem of ad-hoc legal information retrieval, i.e., returning relevant legal resources related to a short user query formulated in natural language. By developing practical and reliable models that assist people with their legal questions, we intend to bridge the gap between citizens and the law.
LECTURE 2
Time: 12:30 - 13:00
Speaker: Hellen van der Kroef
Title: Law, Language and Legal Tech: a New Approach for the European Union
Abstract: The European Union is considered the most complex multilingual organisation worldwide, with 24 official legislative languages. Directives, a subset of EU law, set out a goal to be accomplished but do not directly apply. Each Member State has to adopt its own legislation in order to comply with the directive in a process called transposition. While legal outcomes should be identical, this is far from always the case. This research project aims to get to the bottom of the role of language in transposition, and how non-compliance could be detected through automated approaches using natural language processing.
