2023-2024 / DROI1357-1

European law, (big) data and artificial intelligence applications seminar


24h Th

Number of credits

 Master of Science (MSc) in Data Science5 crédits 
 Master of Science (MSc) in Computer Science and Engineering5 crédits 
 Master of Science (MSc) in Computer Science and Engineering (double degree programme with HEC)5 crédits 
 Master of Science (MSc) in Data Science and Engineering5 crédits 
 Master of Science (MSc) in Computer Science5 crédits 
 Master of Science (MSc) in Computer Science (joint-degree programme with HEC)5 crédits 
 Master in law (120 ECTS)5 crédits 
 Master in political sciences : general (120 ECTS) (in Science, Technology et Societies (STS))5 crédits 
 Extra courses intended for exchange students (Erasmus, ...) (Faculty of Law, Political Science and Criminology)5 crédits 
 Master in multilingual communication (120 ECTS) (Digital media education)5 crédits 



Ljupcho Grozdanovski

Language(s) of instruction

English language

Organisation and examination

Teaching in the first semester, review in January


Schedule online

Units courses prerequisite and corequisite

Prerequisite or corequisite units are presented within each program

Learning unit contents

This seminar, taught in English, pursues two main objectives. First, it will outline and comment the main issues (political, social, economic and legal) related to big data and new technologies, in particular Artificial Intelligence (AI), in the European Union's legal order. Second, it will critically assess the main regulatory approaches adopted by the EU legislator, on points affecting data processing and data protection, as well as the standards imposed on designers and users of data powered technologies (such as AI).

In achieving these objectives, the students will acquire in-depth knowledge of the issues raised by the Big Data phenomenon (and the technologies having emerged as a result), of the advantages and shortcomings of the regulatory solutions given, and gain valuable insights into the future application of the EU's regulatory framework relating to AI.

The seminar will include 24 hours of ex catedra lecutres, structured around four main themes:

Chapter 1: Defining the objectives of the EU's AI regulation - the lectures under this Chapter will address the problem of identifying and selecting the objectives and methods of AI regulation in the EU. In this context, they will raise three main points: 1. a brief overview of the industrial revolutions having led to the emergence of intelligent technologies, 2. the definition of the concepts of AI and regulation, and 3. the procedures put in place within the EU for the purpose of identifying the key objectives that would frame the Union's AI regulation.

Chapter 2: The template for the EU's AI regulation: the GDPR and its progeny - the lectures under this chapter will present and comment on the impact of the RGPD on legislative instruments in the EU relating to new technologies, including AI. To this end, the lectures will address four main points: 1. the GDPR's aim to strike a balance between the free flow of data and data protection; 2. the design of the GDPR, 3. the impact of the GDPR on subsequent legislation on new technologies and 4. the advent of the EU's AI regulation and the inspiration it draws, in terms of objectives and design, from the GDPR.

Chapter 3: The reality of AI use: what topical examples of Ai risks and existing court practice can teach us about the future application of the AI Act - the lectures under this Chapter will present and comment on available examples of practical use and caselaw  dealing with intelligent systems viewed as presenting a (high) risk of harm. Two sets of cases will be distinguished: 1. risks covered by the EU's AI regulation (access to labour, medical diagnosis, biometric identification, use of AI by public authorities, access to social services) and 2. risks not covered by the EU's AU regulations (tacit collusion, autonomous vehicles, taxation and investment, security and defense, art and innovation).

Chapter 4: Judicial redress in cases of harm occasioned by AI systems - The lectures under this chapter will address the issues of evidence, the allocation of liability and effective jurisdictional protection in cases of harm caused by the use of AI systems. It will, in particular, address three main points: 1. the debates (legal but not only) on the ability of AI to autonomously cause harm; 2. the challenges faced by litigants when seeking compensation for harm occasioned by AI use; and 3. the solutions to the said challenges proposed by the EU's upcoming legislation on so-called algorithmic liability.


Learning outcomes of the learning unit

Solid understanding of the approaches and methods followed in regulating AI and big data in the EU.

Solid knowledge and understanding of the regulatory challenges as well as opportunities presented by new technologies, in particular AI.

Development of the ability to discuss in English before an audience comprised of lawyers, political scientists and engineers.

Active participation in debates on the six main themes addressed in the seminar.

This course contributes to the learning outcomes II.1, II.2, V.1, V.2, VI.1, VI.2, VI.3, VI.4, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in data science and engineering.

This course contributes to the learning outcomes II.1, II.2, V.1, V.2, VI.1, VI.2, VI.3, VI.4, VII.1, VII.2, VII.3, VII.4, VII.5 of the MSc in computer science and engineering.

Prerequisite knowledge and skills

Openeness to, and exploration of various aspects of the interrelationship between law and new technologies

Planned learning activities and teaching methods

The seminar will take place in person, with 24 hours of lectures delivered ex catedra. For each Chapter, the students will receive a list of sources (textbooks, articles and judgments) with a selection of mandatory readings. The lectures will be interactive, including discussions between the Professor and the students namely on the sources selected as mandatory. PPTs will be used on specific points of the seminar, and will also be made available to the students.

Mode of delivery (face to face, distance learning, hybrid learning)

Face-to-face course

Recommended or required readings

Relevant materials (presentations, monographs, articles, caselaw) will be made available to the students for each of the four Chapters included in the seminar. A selection of sources for mandatory reading will also be communicated to the students.

Exam(s) in session

Any session

- In-person

oral exam

Work placement(s)

Organisational remarks and main changes to the course

See the mode of delivery tab above


Ljupcho Grozdanovski (lgrozdanovski@uliege.be)


Jérôme De Cooman (Jerome.decooman@uliege.be)

Quentin Bebronne (Quentin.Bebronne@student.uliege.be)

Association of one or more MOOCs

There is no MOOC associated with this course.