Data Law

Our society is experiencing a new industrial revolution that is deeply transforming our economic, social and legal institutions. While information and communication technologies have already become ubiquitous in our lives, advanced methods of statistical analysis and artificial intelligence are increasingly shaping the entire spectrum of our activities, including the ways we socialize, work, travel, and interact with our environment. This revolution is built around one notion: data! The course of Data Law introduces students to these new exciting topics. On the one hand, it studies how state law and non-state tools regulate data processing activities and the applications and technologies built on top of them (e.g., algorithms, AI, Blockchain). On the other hand, the course studies the role of ‘law as data’ by exploring the use and application of AI methods in the field of law.

The course adopts a transnational and interdisciplinary perspective covering regulation in Belgium, Europe and selected key jurisdictions (e.g., North America, China), and incorporates selected technical elements from informatics and data science. The course is project-based: students will deliver a practical output through which they should show to have acute legal knowledge and be conversant with data regulation (notably GDPR and AI Act) and new technologies.

1. The student understands the key legal, technical, and socio-economic issues at stake with the use and processing of data.
2. The student masters the key concepts, structure and methodology of International/European/Belgian Data Protection Law.
3. The student can apply data protection and privacy laws to complex cases.
4. The student understands the key aspects governing data and AI in other branches of law as well as the liability regimes.
5. The student discerns the multifaceted dimensions of data.

Required and Corequired knowledge and skills

No pre-requirement

Each session will consist of a lecture delivered by the instructor and one or several interactive questions. Some sessions will also host guest speakers.

Lecture: The main objective of the lecture method is knowledge transfer, for instance, in relation to the foundations of the course such as e-privacy or GDPR. The lectures will draw on a predefined corpus of legislation, case law and scholarly contributions covering the EU and national countries (including Belgium) and comparative law from non-EU countries. Please make sure to peruse the mandatory readings prior to each session.

Interactive questions: Practical cases and transversal case studies seek to equip students with analytical and argumentation skills to solve concrete problems, for instance, the implementation of a GDPR compliance program, comment of a court decision, the review of a dataset subject to litigation, or exercising the right of access to a data controller in practice. The case studies will connect, when possible, to current events.

References, bibliography, and recommended reading

• Mac Macmillion, “Data Protection Concepts”, in Eduardo Ustaran (ed.), European Data Protection. Law and Practice, IAPP, 2018

• David Restrepo Amariles, “Algorithmic Decision Systems: Automation and Machine Learning in the Public Administration”, (2020) The Cambridge Handbook of the Law of Algorithms,

• David Restrepo Amariles and Damien Charlotin, Legal Analytics for Risk Management and Compliance, Handbook on Law and Management, Oxford University Press (Forthcoming, 2024)

• Rajaa El Hamdani, Majd Mustapha, David Restrepo-Amariles, Aurore Troussel, Sébastien MeeùsAnd Katsiaryna Krasnashchok, “A Combined Rule-Based and Machine Learning Approach for Automated GDPR Compliance Checking”, Proceedings of the Nineteenth International Conference on Artificial Intelligence and Law (ICAIL), São Paulo, 2021

• Kevin Werbach, “Trust, but verify: why the blockchain needs the law”, Berkeley Technology Law Journal, Vol. 33, No. 2 (2018), pp. 487-550

Course notes

• Université virtuelle

Campus

Solbosch

Method(s) of evaluation

• Project
• Other

Project

Other

1. Group Case Study
2. Individual Assignment

Mark calculation method (including weighting of intermediary marks)

1. Group Case Study (60%) 
2. Individual Assignment (40%)

Language(s) of evaluation

• english