LAW AND TECH

In recent years, emerging technologies such as artificial intelligence (AI), blockchain, large language models (LLMs), and quantum computing have started reshaping how law is created, applied, and practiced. Legislatures increasingly draft algorithmic and computable rules; courts and administrative bodies experiment with AI-based tools to support their decisions; and legal professionals rely more and more on legal tech to conduct research, analyze contracts, and manage disclosure obligations. At the same time, the EU has adopted major regulatory reforms—such as the Digital Services Act (DSA) and the Artificial Intelligence Act (AIA)—to govern digital platforms and AI systems through a risk-based, rights-respecting framework, complemented in Italy by its first national AI law. This course provides students with the theoretical foundation and technical literacy needed to understand these transformations and to critically identify, evaluate, and address the legal challenges raised by emerging technologies. Uniquely, it combines a legal track—covering legal tech, "law as code," "law as data," algorithmic disclosure, judicial use of AI, the DSA, the AI Act, and the Italian AI regime—with a technical track that covers the mechanics of the Internet, legal databases, information security fundamentals, AI, and blockchain. This dual approach ensures students gain the technical grounding necessary to engage with complex legal questions in an informed way. A series of guest lectures from prestigious academics and practitioners in tech contracting, cryptocurrency regulation, and intellectual property rounds out the course with broad theoretical insights and applied, professional perspectives. Being able to critically identify, evaluate, and analyze the technical and legal challenges brought by new technologies—and to apply legal principles to address them while truly understanding the underlying technology—is an increasingly vital skill in today's job market.

No specific technical or legal prerequisites are required. The technical foundations needed to engage with the course's legal content—how the Internet works, information security, and the basics of AI and machine learning—are built progressively throughout the course alongside the legal analysis. Nonetheless, students are encouraged to read the following article before the course begins: R. Whalen, "Defining legal technologies and its implications", International Journal of Law and Information Technology, 2022, 30, 47-67, available at https://journalcrcl.org/crcl/asicle/view/7/3.

By the end of the course, the acquired knowledge and skills will enable students to better understand how digital technologies shape various fields of law and how AI and digital markets are regulated. Knowledge and understanding: Through lectures, interactive activities, and in-depth seminars, students will learn to navigate the "Law and Tech" debate effectively, acquiring a comprehensive understanding of technological progress's impact on legal disciplines and digital markets governance. Ability to apply knowledge and understanding: Students will be able to identify legal issues surrounding new technologies, recognizing the underlying European and national regulatory perspectives. They will also master key legal concepts essential for contextualizing new technologies. These skills will be assessed through the mid-term and final written exams, as well as in-class presentations. Autonomy of judgment: Students will develop independent evaluative and analytical reasoning regarding course topics. They will accurately identify the legal macro-categories needed to solve technological legal problems. Critical thinking will be cultivated through short essays submitted via LUISS Learn and in-class group presentations. Communication Skills: Students will master specialized Law and Tech vocabulary to construct persuasive, effective arguments aligned with current legal developments. In-class presentations will allow students to present oral reports on assigned topics based on prior written research. Learning skills: Students will be equipped to identify future development trends in the Law and Tech field, preparing them for advanced academic study and professional practice.

The course combines legal and technological approaches (reflecting the complementary expertise of the instructors) and covers the following areas: Legal track (Prof. Di Porto): Covers legal tech, "law as code," "law as data," algorithmic disclosure, judicial use of AI, computational analysis of disclosure duties and rulemaking, the DSA, the AI Act, and the Italian AI regime. Technical track (Prof. Iecher): Covers the Internet, legal database research, information security, AI/machine learning fundamentals, prompting, APIs, blockchain, and digital productivity. Guest Lectures & Presentations: Guest sessions with leading academics and practitioners are integrated throughout the semester, concluding with student group presentations.

All teaching materials are available on the MyLuiss platform from the start of the course. Readings are provided as uploaded PDF files or linked via the Luiss Electronic Library and external repositories. Refer to the course schedule for detailed weekly reading assignments.

Lectures, seminars, in-class group presentations, and structured material discussions are utilized to encourage active debate and foster autonomous critical thinking. Group formation: During Week 1, students must form groups of up to 4 members. Each group will be assigned a presentation time slot to ensure clear scheduling from the outset. Topic choice: Each group selects a topic in consultation with the TAs. The topic must: Bridge both the legal and technical tracks. Expand beyond the mandatory course reading materials. Topics must be confirmed with the TAs by Week 1. Paper draft: Draft a short research paper (5–10 pages) analyzing the selected topic systematically (framing the issue, analyzing key challenges, evaluating arguments pro/con, and proposing conclusions). A template of previous successful papers is available on the course platform. In-class presentation: Groups present their paper to the class on their scheduled date. Every group member must present a section of the paper and actively participate in the subsequent Q&A session. Deadlines: Week 1: Form groups and register member names with the TAs. Week 2: Agree on the topic and paper outline with the TAs. Paper Submission: Submit the final paper draft to the TAs at least 4 days prior to the presentation date. How to present and assessment criteria (35%): Group presentation in class (typically 20 minutes). Interactive discussion with instructors, TAs, and classmates (typically 10 minutes). Classmates are required to read papers in advance and actively participate in the Q&A. Contributing to discussions on peers' papers counts toward the Active Participation grade (10%). Assessment criteria for in-class presentations: Graded on an A–D scale. Final presentation grades will be published on MyLuiss after all group presentations have concluded. Luiss Policies for AI Generative Tools: see details on MyLuiss

Attending students: [Attendance is monitored via the Beacon app; students must attend at least 70% of classes to qualify] 25% Mid-term multiple-choice test [consisting of 30 questions in 20 minutes; if failed, it can be re-taken only once, together with the Final multiple-choice test; the grade taken cannot be rebutted 35% In-class presentation (see guidelines below) 10% Active participation 10% Attendance 20% Final multiple-choice test [25 questions in 20 minutes] Non-attending students: 100% Multiple-choice test [65 questions in 50 minutes].

Thesis supervision is granted based on achieving a strong final grade in the course and submitting a well-crafted, relevant research topic proposal.