| Description |
This lecture introduces the technical foundations of modern AI in a form tailored to legal practice and examines how AI systems are governed through private law and emerging regulation. The course begins with an accessible technical introduction, focusing on how AI systems are built, deployed, and evaluated, and which design choices tend to generate legal risk.
The second session focuses on generative AI, large language models (LLMs) and agents building on them, with particular attention to responsible and effective use in academic writing and legal research. We address capabilities and limits, verification and citation practices, confidentiality, bias, and research integrity, and we translate these issues into concrete duties and risk controls relevant to legal work.
The remaining sessions connect technical realities to doctrinal and regulatory questions. We explore how AI affects liability and causation, product and service qualification and allocation of risk, consumer protection, and data protection. Throughout, the course emphasizes how compliance obligations and enforcement trends interact with private-law remedies and litigation risk.
National and international regulatory developments are covered comparatively, with a focus on private-law relevance, e.g. governance duties, transparency, documentation, incident handling, and downstream allocation of responsibility in supply chains and platform ecosystems. Key reference points include the national law, the EU AI Act and GDPR, alongside comparable approaches in other jurisdictions and international standards and soft-law instruments.
Guest speakers (tba) will illustrate how legal analysis maps onto system design, deployment constraints, and operational decision-making. The course is taught as a lecture with interactive problem-based segments and concludes with a final exam assessing conceptual understanding, statutory/regulatory analysis, and applied issue-spotting. |
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