- For students attending the course (frequentanti), the detailed course content corresponds to the list of readings and materials (Link) with the possibility of replacing some contents with new topics discussed during the lectures.
- For students not attending the course (non frequentanti) the detailed course content corresponds to the Handbook “Artificial Intelligence and Legal Analytics” (Ashley 2017), Cambridge University Press (Link).
Erasmus students follow the same programme of regular students.
For students coming from other degree courses, that have already passed an exam of Legal Informatics, the course contents for possible integration shall be agreed with the professor.
The course is divided into two parts: Computable Law; Legal issues of AI and autonomous systems.
COMPUTABLE LAW:
Introduction to computable law: the enablers; main approaches and methods
Legal retrieval systems (all different systems and approaches): introduction to legal information retrieval, new trends, case studies
Man-made models (man-made ontologies, norms, rules, case based reasoning, argumentation): legal knowledge representation (logic, ontology and computation); case studies
Machine learning systems: introduction to big data and the law, legal text analytics; case studies
LEGAL ISSUES OF AI and AUTONOMOUS SYSTEMS:
Legal issues of AI: Big data, Algorithmic decision making, algorithmic justice: predictive systems and the issues of fairness and transparency, data protection
Legal issues of autonomous systems: automation in socio-technical systems; liability and automation; task-responsibility, main kinds of involved liabilities: personal liability, enterprises liabilities, product liability, liability of standard setters. Cases studies: aviation, autonomous driving, health care, robot, autonomous agents.
Internet, Law, Society 