Duration
24h Th
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
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: Selecting the Appropriate Liability Model in the Field of AI - The lectures in this chapter will pursue three objectives: (1) to critically examine the contemporary challenges that AI raises regarding the proof of harm and causation, as well as the fair allocation of risks arising from the use of AI systems; (2) to provide historical context on the ways in which traditional liability law and doctrines have responded to previous industrial revolutions, particularly industrialization and automation; and (3) drawing on these historical examples, to provide a structured and critical analysis of the expert and regulatory debates at the EU level concerning the most appropriate liability model for AI technologies.
Chapter 4: Operationalizing AI liability in EU Law - Against the backdrop of the developments discussed in Chapter 3, the lectures in this chapter will provide a critical and detailed examination of the EU law provisions that have operationalized these discussions. The two seminal legislative instruments in this context are the AI Liability Directive (AILD), which was ultimately withdrawn and did not become binding, and the Revised Product Liability Directive (R-PLD), which is currently in force. These two instruments will be examined along three key dimensions: (1) the models of liability on which each instrument is based; (2) how each instrument allocates the burdens of proof; and (3) the selection of facts that may be presumed, considering the practical difficulties in establishing causal links in cases involving opaque AI technologies. The lectures in this chapter will conclude with a critique of the limited scope of these instruments, both in terms of the types of disputes to which they are likely to apply and the types of liability excluded from their scope, namely liability arising in the field of defence.
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
Course materials and 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
written exam ( multiple-choice questionnaire, open-ended questions )
Work placement(s)
Organisational remarks and main changes to the course
See the mode of delivery tab above
Contacts
Lecturer:
Ljupcho Grozdanovski (lgrozdanovski@uliege.be)
Jérôme De Cooman (Jerome.decooman@uliege.be)
Association of one or more MOOCs
There is no MOOC associated with this course.