This laboratory is the third 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 Laboratory intends to provide students with the fundamental notions of a discipline that has assumed absolute centrality in the scientific and legal debate. However, in order to fully understand their legal implications, students must first become familiar with the language and logic of probabilistic and statistical inferential algorithms, as well as their practical applications. In addition, students will address the topic of machine learning and algorithmic simulation of neural networks.

Knowledge and comprehension: to achieve knowledge of some cutting-edge topics in the relevant field of study with the appropriate teaching support, general understanding of the theory of algorithms in computer science, probabilistic logic and the main types of inference algorithms. Applied knowledge and comprehension: understanding how machine learning works by simulating neural networks. 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. Introduction to artificial intelligence II. Algorithms III. Machine learning

The teaching material consists of the content of the lessons given by the lecturer, related handouts and other materials shared on Luiss Learn. G.D’Acquisto – Intelligenza artificiale, Giappichelli Recommended readings: Jerry Kaplan, Intelligenza artificiale. Guida al futuro prossimo, Luiss University Press

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) 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.

The competences are assessed via an oral and a written test. 50% of the final grade will be given by the theoretical part and 50% of the final grade will be given by the practical part.