Submissions should include the title of the work, a 500-word (maximum) abstract, 5 keywords, author’s full professional affiliation, CV (or resume), and contact information. Submissions should be sent by e-mail to firstname.lastname@example.org and the subject line of the e-mail should read as follows: “2019 GLAIC – [Author’s First and Last Name]”. The deadline for the submission of abstracts is November 30, 2018, selected participants will be notified by December 14, 2018.
Title: Toward Law’s Network: a metadata model
5 keywords: networks; empirical legal research; citation analysis
contact information & affiliation
Dr. Olivier Charbonneau, LLD
Associate Librarian, Concordia University
Doctor of laws, Centre de recherche en droit public, Université de Montréal
1455 boul. de Maisonneuve West, Office S-LB-505-6; Montréal H3G 1M8
514-848-2424 ext. 7362
CV is attached
The burgeoning and ebullient field of machine learning and algorithmic analysis introduces many possibilities for the development of new legal tools, methods and, possibly, institutions. It would seem that, before legal professionals can realistically grasp these new opportunities, one would need a roadmap to better understand which set of approches will lead to meaningful developments and improvements for all.
Toward this end, we will present a conceptual model employing network analysis to explore how meaning could be derived from various corpora of primary and secondary legal sources. We hope this conceptual model can act as a catalyst to empower legal and computer professionals in meaningful exchanges. By conceptual model, we mean the broad sketches which represent how computer scientists can implement a situation into code. We draw inspiration from participatory design in the field of human computer interaction. Similarly, networks resonate both in machine learning as well as in the humanities and the social sciences. We will start our journey in the HSS as this is the field with the most pressing need to understand networks at the theoretical level. Finally, we will explore how laws, rulings and doctrine are linked, essentially deriving a metadata network model of legal sources.
0.0 Introduction: Law’s network
1.0 Canadian legal system
1.1 Why the (messy) Canadian legal system is a unique laboratory for machine learning
1.2 Open access as an advantage
2.0 Deconstructing law
2.1 Within law: primary sources
2.2 Around law : doctrine (dictionaries & treaties) and commentary
This presentation will focus on the « Semantic analysis and interpretation of legal texts » as set forth in the third conference theme (« 3. Artificial Intelligence, Empowerment of Legal Profession and Law Enforcement »). Preliminary versions of this talk were delivered at the École thématique CNRS sur l’Analyse de réseaux et complexité in 2018 and the 9e journée de jurilinguistique at McGill University in 2016.
Ce contenu a été mis à jour le 25 février 2019 à 10 h 01 min.