This laboratory is the first 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 Laboratory of Language and Logic of Machines aims to form the foundation of such complex knowledge, providing the vocabulary, grammar and syntax of computational thinking, programming, coding and cryptography.

Knowledge and comprehension: to achieve knowledge of some cutting-edge topics in the relevant field of study with the appropriate teaching support, basic understanding of programming languages and knowledge of computer architecture and networks. Applied knowledge and comprehension: ability to use coding to achieve effective results. Making autonomous judgements: collecting and interpreting relevant information and data. Communication skills: communicating information, ideas, problems and solutions. Ability to learn: having developed the necessary skills to undertake subsequent studies with a high degree of autonomy.

I. Logic and computational thinking II. Programming languages and coding III. Hardware and software architecture IV. Data protection and information security

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: G.D’Acquisto, M. Naldi, Big Data e privacy by Design, Giappichelli G.D’Acquisto, D.Benedetti, L.Nobile, Innovazione tecnologica per umanisti, Giappichelli Jake VanderPlas, A Whirlwind Tour of Python, ed. O'Reilly Dennis Curtin, Kim Foley, Kunal Sen, Cathy Morin, Agostino Marengo e Alessandro Pagano, Informatica di base, McGraw-Hill

Acquisition: lectures, podcasts and online quizzes Practice: guest speakers and coding simulations Investigation: analyzing ideas and information in a range of materials and resources, using conventional methods 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.