Instructional goals
This course represents the final teaching module of the overall educational structure dedicated to legal informatics and is composed of five preparatory laboratories and a final course distributed across the first three years of the five-year integrated master's degree program in Law at LUISS. The laboratories and the final course on Intelligent Machines and Law constitute a unified and progressive pathway designed to develop interest and necessary knowledge about the complex interaction between informatics and law. The intent is to train jurists ready to face the challenges related to the legal consequences of the use of information technologies in every sector of social and professional life, and, considering the interdisciplinary nature of the subject, to address the "new rights" connected with the increasingly widespread use of machines capable of self-learning. For this reason, the course analyses the legal profiles relating to liability for autonomous vehicle guidance systems, automatically executing contracts, new personal rights related to profiling and so-called credit scoring, applications serving judges and judicial administration (including in the criminal sector), with particular attention to European legislation and Italian and international jurisprudence. Students must therefore acquire the technical and legal knowledge necessary to understand complex phenomena such as the conditioning of law by technology and the legal problems posed by the use of artificial intelligence in legal professions, in the field of medicine, and in economic and social sciences. This represents a strategic objective in the educational activities of the Law Department, since full knowledge of information technologies and their disruptive impact on society, law, economics, and institutions at a global level constitutes an indispensable foundation in preparing future jurists. The technological revolution of recent years requires, in fact, professional figures capable of applying traditional legal categories to entirely unprecedented phenomena and of devising, when necessary, new solutions suitable for regulating reality according to the general principles of the legal system. This requires, first and foremost, an understanding of the basic architecture of networks, the languages of mathematics, and the logic of algorithms, in order to fully evaluate their impact and legal consequences on fundamental personal rights. The Course on Intelligent Machines and Law, as the culmination of this three-year pathway of technical and legal studies, therefore represents the moment of synthesis of the knowledge acquired during the three years through technical laboratories and previous courses.
Prerequisites
You need to pass both the digital skills test and the preparatory examination in Digital Law and Data Protection before taking the final exam.
Intended learning outcomes
Knowledge and understanding: students must acquire full knowledge of some fundamental topics in the subject, of the scientific debate regarding the application of A.I. in economic, social and institutional contexts, and of the complex issues related to the application of law to intelligent machine activities in different fields of application. Applied knowledge and understanding*: students must acquire the ability to conceive and support arguments relating to the legal aspects of artificial intelligence applications in different fields of application (in particular: the field of civil, criminal and administrative law and in the medico-legal, judicial, robotics and autonomous transport sectors). Independent judgment: students will be able to collect and interpret information, scientific and legal data relevant to the subject. Communication skills: students will acquire the ability to communicate information, ideas, problems and solutions in the field of legal informatics, using appropriate scientific and legal terminology. Learning capacity: students, upon completion of the course, will have developed the necessary skills to undertake subsequent studies with a high degree of autonomy.
Course Contents
Legal rationality and artificial rationality - Principles for algorithmic decisions - Applications - The intelligence of man and machine - Responsibility for production and management of AI - Self-driving cars - Sentient robots - Responsibility of the owner - Responsibility of the manufacturer - Responsibility "from defective algorithm" - Big data - AI, free movement and personal data - The problem of values: The CEPEJ Ethics Charter on the use of AI in judicial systems - European Parliament Recommendation 051/2017 - The European Commission’s proposal for a regulation on I.A. (21/4/2021) and on robotics - AI and criminal justice.
Reference Books
The teaching material consists of the content of the lectures given by the teacher, documents and other audiovisual materials shared on Luiss Learn. Recommended text: G. Buonomo, G. Ciacci, A. Costanzo (eds.), Macchine intelligenti e diritto, Giappichelli, Torino, 2025.
Teaching Methods
Frontal and distance teaching; individual exercises and group work (papers, role-playing and simulations on cases taken from Italian and international judicial news); analysis of judicial cases; expert testimony; presentations and assigned research with production of reports, articles and essays.
Assessment Method
The evaluation system aims to reward students' attendance and active participation in lessons. The final grade, expressed on a scale of thirty, depends on attendance, active participation in class, the results of the midterm exams, and the outcome of the final oral exam. There are two midterm exams: - The first, after the first part of the course, involves studying a legal case and solving an open-ended question. - The second, scheduled at the end of the course and reserved for attending students, consists of a multiple-choice questionnaire with 30 questions. Only students who have attended at least two-thirds of the lessons are eligible to take the second midterm exam, which grants access to the simplified oral exam. Justified absences, due to study or health reasons, must not exceed 50% of the lessons or teaching modules. Students who score at least 26 out of 30 will only need to answer questions in the oral exam on topics where they gave incorrect responses. The simplified exam is valid only for the first session in which the student chooses to participate.
Thesis assignment criteria
To be assigned a thesis, the candidate must have passed the exam with a good score after attending the lessons. The originality of the chosen topic and the experimental nature of the selected research method will be evaluated.
Week 1
Introduction to the study of the subject – Basic concepts: self-learning, general AI, inference and inductive process – Towards the “lex robotica” - The question of values. Rules and learning: law and AI – Machine learning.
Week 2
The intelligence of man and the machine: the differences – The “subjectivity” of machines - Machine learning and neural networks – Symbolic systems and sub-symbolic systems – The two major limitations: understanding data quality. The role of jurists. The evolution of the human brain ad intelligence – How to compare an intelligent machine – Emotions and intelligence.
Week 3
Disruptive and enabling technologies for legal professions – Some legal apps: eBrevia, Kirasystems, Luminance – AI as “predictive” tools The Loomis case and A.I. tools to support judicial activity - The European Ethical Charter and the five principles: fundamental rights, non- discrimination, data quality, transparency, control.
Week 4
“Strong” and weak algorithms - Law and “product liability” – The liability “from algorithm” - The theory of “human in command” - AI and industrial production: the process of invention assisted by AI and self-learning AI - Introduction to the liability for AI development and maintenance: the defective product - Driverless vehicles – vehicles driven by AI – the driver’s, manufacturer and programmer liability.
Week 5
Introduction to robotics - Autonomous machines and thinking machines - The Delvaux Report: towards "subjectivity" of machines - The problem of the Global Repository of Intelligence - Recommendation 051/2017 of the European Parliament - EU regulation on the I.A. approved on 13/3/2024
Week 6
The principle of liability – The Bioethics Committee Report – Self-driving cars and civil liability – High-risk machines – Introduction to Robotics – The measure of a man: self-awareness, intuition and vital momentum.
Week 7
Product liability and safety of the A.I. products – Defective product liability applicable to intelligent machines – "Algorithm" liability – Artificial intelligence and dangerous activities – Study of a court case.
Week 8
Artificial intelligence and contracts – The control and predictability of AI based contracts – Tomorrow’s contracts (and the future of contracts) – Contracts concluded by intelligent machines – Contracts for A.I. – contracts produced by A.I
Week 9
Algorithmic contracts – The negotiatori algorithm - The robotic decision - Judicial Administration and AI: the intelligent machine at the service of the judge - “predictive” judicial applications.
Week 10
Legal certainty and interpretation of rules: the functions of intelligent machines. The robotized administrative action and the Human-in-command principle – New landing zones in administrative court cases.
Week 11
Artificial intelligence and criminal justice - The problem of ethical values: CEPEJ’s Ethical Code on the usage of AI in judicial systems – The EU Parliament’s LIBE committee’s study on the impact of AI on judicial systems. Security measures and penalty application – Current limitations in using intelligent machines in application of sanctions.
Week 12
The COMPAS algorithm and the social danger prediction - AI systems and the trial – The algorithms of the telematic trials – Intelligent machines and pre-trial investigations.