REGULATION FOR AI

The course provides students with a solid understanding of how artificial intelligence systems are shaped by data and how data law functions as a core tool for governing AI. Students will critically engage with data protection law and digital regulation in the context of the information economy, across jurisdictions. The course explores how AI challenges foundational principles such as transparency, non-discrimination, and accountability. Particular attention is given to the evolution of harm and liability in data-driven and automated environments.

None.

By the end of the course, students will have acquired a solid understanding of the foundations of European and comparative data protection law, of the main principles governing the processing of personal data, and of the emerging framework of European AI regulation. They will be able to interpret and critically discuss the principal legal issues raised by big data, artificial intelligence, digital services, and digital markets, with particular attention to transparency, explainability, accountability, profiling, tracking, scoring, market power, manipulation, and discrimination in digital environments. More broadly, the course will enable students to develop the ability to analyse the interaction between technological innovation and legal regulation, and to assess the implications of data-driven and AI-based systems for individual rights, market dynamics, and public enforcement.

• Background and foundations of EU and comparative privacy/data protection law • Key principles and rules of European data protection and enforcement • Key principles and rules of European AI governance • Main regulations on digital services and digital markets • Impact of big data and artificial intelligence on transparency, explainability, accountability, purpose limitation, anonymisation and self-determination • Profiling, scoring and tracking mechanisms in digital markets • Platforms’ market power and consumer personal data issues • Risks of manipulation and discrimination in digital environments

Teaching materials will be provided throughout the course, including slides, selected readings, and additional resources.

Lectures and case studies.

• Mid-term written exam (1/3) • Final written exam (2/3).

Thesis topics will be assigned based on students’ interests and performance during the course, as well as on the relevance and feasibility of the proposed research topic.