ARTIFICIAL INTELLIGENCE AND LAW

The course aims to provide students with systematic knowledge of artificial intelligence, its legally relevant applications, and its regulation, as the concluding segment of the progressive educational pathway devoted to the relationship between intelligent machines and law. The course seeks to develop students’ ability to understand the logic of machine learning processes and autonomous algorithmic decision-making, while fostering the acquisition of the theoretical, technical, and legal skills needed to assess the implications of AI across the different domains of contemporary legal experience. The course addresses the essential concepts of AI theory, natural and formal languages, as well as the logical foundations of algorithms and machine learning, together with the practical implications of AI ethics. It also introduces the national, European, and international regulatory framework for artificial intelligence, with particular reference to the European strategy, the AI Act, the Italian law on AI, and the main multilateral and comparative approaches. Through an empirical and laboratory-based approach, grounded in the analysis of practical cases, concrete applications, regulatory scenarios, and emerging legal issues, a specific part of the course is devoted to examining the implications of AI across different areas of law. The objective is to train jurists capable of critically interpreting and operationally applying legal categories to the phenomena of artificial intelligence, while understanding both the technical foundations of autonomous systems and the regulatory, protective, and liability-related needs arising from the use of intelligent machines in digital society.

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Knowledge and understanding: students will acquire systematic knowledge of artificial intelligence, its technical foundations, and its main legal, social, and economic implications. Applied knowledge and understanding: students will apply the knowledge acquired to the analysis of practical cases and AI applications across different areas of law, identifying legal issues and possible regulatory solutions. Making judgements: students will collect and interpret relevant information, data, and sources, critically assessing the impact of AI systems on rights, liability, institutions, and markets. Communication skills: students will communicate problems and solutions relating to artificial intelligence and its regulation, using appropriate technical-legal language. Learning skills: students will develop the skills necessary to continue independently the study of the relationship between intelligent machines and law and to keep their knowledge up to date in light of technological and regulatory developments.

1. Introduction to and Theory of Artificial Intelligence • essential definitions • cognitivism (strong AI and A. Turing) and behaviourism (weak AI and J. Searle) • natural languages and formal languages 2. Algorithms and Machine Learning: Logical Foundation (students should be able to understand the logic underlying autonomous learning and decision-making processes) • supervised and unsupervised learning • generative AI • predictive algorithms • biases and hallucinations • neural networks 3. Rationality and Algorithmic Decision-Making • transparency as “explainability” • fairness • quality • responsibility • human-centricity 4. Law and Regulation of Artificial Intelligence • the European strategy and the AI Act • the Italian AI Law • main multilateral international approaches(UN and OECD) and main comparative approaches (US and PRC) (topics may be selected according to the Chairs’ preferences, provided that the approach remains empirical and laboratory-based, moving from the specific practical case to the broader issue or principle. Possible areas may include:) • criminal law (criminal liability, predictive policing, and predictive justice) • civil law (legal personhood and algorithmic civil liability) • autonomous driving • intellectual property and generative AI • labour law • marketing • algorithmic democracy and elections • AI and legislation • algorithmic administration • autonomous weapons systems

Teaching materials consist of the content of the lectures delivered by the instructor, the related handouts, and other materials shared on MyLuiss. Recommended readings (mandatory for non-attending students): G. Corasaniti Datascience e diritto ,certezze artificiali e benefici del dubbio 2022 ; Sicurezza informatica e intelligenza artificiale Rischio e resilienza nello spazio giuridico europeo Giappichelli 2025; G. Corasaniti , Cyberetica Luiss University press 2026

Learning: lectures and online quizzes Practice: guest lectures by experts, case studies, and simulations Inquiry: analysis of ideas and information across a range of materials and resources, using legal databases to collect and analyse data and compare texts Collaboration: small-group work, discussion of peers’ findings, and development of shared outcomes Discussion: seminars and in-class group discussions Production: essays, reports, and presentations

The final grade, expressed on a 30-point scale and included in the overall grade point average, will be determined on the basis of the following components and corresponding percentages: 75% assessment of coursework completed during the course (TBD) 10% active participation in class 15% final examination (either written or oral)

Exam grade obtained for the proposal of an experimental or original topic