LEGAL INFORMATION AND TECHNOLOGY INNOVATION LAW

The laboratory aims to provide students with essential and applied knowledge of legal informatics and the law of technological innovation, as the first segment of a progressive educational pathway devoted to the relationship between intelligent machines and law. The course seeks to develop students’ ability to understand the interaction between digitalization, artificial intelligence, and legal phenomena, while fostering the acquisition of the legal, technical, and operational skills needed to assess the impact of emerging technologies on law, society, markets, and institutions. Through a laboratory-based approach grounded in the analysis of practical cases, the guided use of digital tools, and the concrete application of legal categories to technological phenomena, the course addresses the foundations of legal informatics, the evolution of the field, the essential elements of formal and algorithmic logic, as well as the proper and functional use of artificial intelligence in legal study and research. The laboratory also introduces the main areas of digital innovation relevant to law, following a method that moves from specific cases to the identification of applicable legal issues, principles, and rules. The objective is to train jurists capable of critically interpreting and operationally applying legal categories to the phenomena of digital and technological innovation, while understanding the role of digital tools, algorithmic logic, and artificial intelligence in the transformation of contemporary legal experience.

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Knowledge and understanding: students will acquire a basic knowledge of the main topics of legal informatics and the law of technological innovation, understanding the legal implications of digital technologies, artificial intelligence, and their applications in the legal field. Applied knowledge and understanding: students will apply the knowledge acquired to the analysis of practical cases, identifying the relevant legal issues and developing coherent arguments in support of possible solutions. Making judgements: students will collect, select, and interpret information, data, and legal sources through databases and digital tools, critically assessing the implications of the technologies examined. Communication skills: students will communicate clearly and appropriately information, problems, and solutions relating to legal informatics and technological innovation, using adequate technical-legal language. Learning skills: students will develop the skills necessary to continue independently the educational pathway in intelligent machines and law and to update their knowledge in light of technological and regulatory developments.

1. What is legal informatics Notion and classification Historical evolution of the subject The teaching of Legal Informatics and digital competences (DigComp 3.0) Elements of formal logic (e.g. propositions, connectives, elementary inferences, essential theorems, decidability, and algorithms). 2. AI Literacy Use and applications of generative AI for law students, using Google AI models. 3. Legal applications and implications of digital innovation: cryptography and digital signatures the legal value of electronic documents blockchain, cryptocurrencies, tokens, and NFTs smart contracts

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.Ciacci-G. Buonomo, “Profili di Informatica Giuridica”, capitoli 1, 3 e par. 3 e 7 del capitolo 4 -L.Capitanio “Laboratorio di Intelligenza Artificiale : Elementi di Intelligenza Artificiale con applicazioni in Python per studenti di Giurisprudenza”, https://amzn.to/4e7Iipj - L.Capitanio “Diritto e Tecnologia del Web3”, https://amazon.it

Learning: lectures, podcasts, and online quizzes Practice: case studies and simulations Inquiry: analysis of ideas and information across a range of materials and resources, using the LLMs authorized by the University to collect and analyze data and compare texts Collaboration: small-group work, discussion of peers’ results, and development of shared outcomes Discussion: seminars, in-class group discussions, online forums, and synchronous and asynchronous discussions

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 on my.luiss.it; 10% active participation in class; 15% final examination on my.uiss.it.

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