This laboratory is the fourth stage of the overall didactic structure of Legal Informatics of the LUISS master's degree in Law, which is made up of five preparatory laboratories and a course spread over the first three years. These teaching classes constitute a unitary and progressive path according to a logical itinerary studied in order to implement the student's sensitivity for the mutual interaction between information technology and law. The intent is to train a jurist who can be ready to face the legal challenges of the digital dimension, increasingly pervasive and transversal in every professional sector, and of IT applications in the legal sector. To this end, the student will also have to acquire purely technical and IT knowledge to fully understand the technological phenomena of which he or she may be required to evaluate the legal implications and effects. This is a strategic goal that the Department of Law has set itself, as it is impossible to imagine the figure of a jurist today who is not fully familiar with digital tools and is unable to analyze the impact of the most disruptive technological applications on society, law, markets and institutions at a global level. The overwhelming innovation encouraged by national and European public policies requires versatile professional figures, capable of applying the traditional categories of law to unprecedented technological phenomena, or even of building new ones better able to regulate the present. To do this, it is required an understanding of the basic architecture of networks, as well as the languages of mathematics and the logic of algorithms, in order to be able to read them in the forms of law. Specifically, the Artificial Intelligence, Machine Learning and Law Laboratory serves as a direct complement to the immediately preceding laboratory, focusing on the legally relevant aspects concerning the implementation of AI. In particular, the course is aimed at: re-reading the general theory of artificial intelligence from a legal perspective, in the wake of the subject that still encompasses the teaching of legal informatics, i.e. philosophy of law; therefore to demonstrate the applicability of artificial intelligence algorithms to the various traditional functions of law; and finally to provide students with an overview of the main experiences of AI regulation at national, European and global level.

Knowledge and comprehension: to achieve knowledge of some cutting-edge topics in the relevant field of study with the appropriate teaching support, understanding the difference between the logic of human intelligence and that of artificial intelligence in relation to the formation of a decision and the social and legal impact of AI applications, understanding the application of different types of algorithms for legally significant purposes and knowledge of the main regulatory instruments existing and proposed for the regulation of AI. Applied knowledge and comprehension: devising and supporting arguments relating on the one hand to the applications of AI in the legal sector and on the other to the major challenges of AI regulation. Making autonomous judgements: collecting and interpreting relevant information and data. Communication skills: communicating information, ideas, problems and solutions with the specific technical vocabulary of artificial intelligence. Ability to learn: having developed the skills necessary to undertake subsequent studies with a high degree of autonomy.

I. Theory of artificial intelligence and law II. The different types of artificial intelligence and machine learning, fair division algorithms and the law III. Law and regulation of artificial intelligence

The teaching material consists of the content of the lessons given by the lecturer, related handouts and other materials shared on Luiss Learn. Recommended readings on Luiss Learn

Acquisition: lectures, podcasts and online quizzes Practice: guest speakers, case study and simulation Investigation: analyzing ideas and information in a range of materials and resources, using legal databases to collect and analyze data and comparing texts Collaboration: small group project, discussing others’ output and building joint output Discussion: seminars, group based class discussion, online forums and synchronous and asynchronous discussion Production: essays, reports, presentations and blogs

The final grade, expressed out of 30, will derive from the evaluation of the following items for the respective percentage share: 20% attendance 10% active participation during classes 50% intermediate tests 20% final exam (written and oral) At the end of the course, an assessment TEST (individual or group) will be carried out. This will not necessarily be a multiple-choice TEST but may be a simulation (with students divided into groups) using an AI system (chosen by the teacher) to simulate its use in a professional context. N.B. The grade obtained at the outcome of the exam of this Laboratory will participate for the share of 1/7 in the final grade which will be attributed to the outcome of the exam of the Macchine intelligenti e diritto (MID1) course and which regularly falls within the curricular average grade of each student.

Class attendance, group work on AI systems (with or without multiple-choice tests in class) and passing the oral exam.