Note: The course is of interest also to students from the faculties of Governance and Global Affairs (Institute of Security and Global Affairs), Humanities (Institute for Philosophy), and the School of Law.
Law, Artificial Intelligence, Discrimination and Bias, Algorithmic Decision-Making, Responsibility, Safety, European Law, Privacy, Data protection, Dignity.
Law, Public Policy, Engineering, Philosophy.
The integration of artificial intelligence (AI) technologies in society is accelerating. AI-based systems can be software-based, acting in the virtual world mainly with some physical support, such as voice assistants like Alexa or Google Home, image analysis software used for diagnoses, recommendation systems for mortgages, crime, or patient management, search engines, or speech and face recognition systems. AI can also be embedded in more complex hardware devices that operate in the environment, such as advanced robots, self-driving cars, drones, or Internet of Things applications.
The High-Level Expert Group on Artificial Intelligence appointed by the European Commission defined AI as 'systems that display intelligent behavior by analyzing their environment and taking actions – with some degree of autonomy – to achieve specific goals.' These technologies process vast amounts of data, can learn from experience and self-improve their performance, which challenges the applicability of existing regulations that were not designed for progressive and adaptive AI. Since the automated processing of data that will evaluate, analyze, and predict outcomes that may affect the privacy, safety, or dignity of individuals, there is a growing interest in understanding what are the legal and regulatory implications of the use of AI in society.
In this course, we first map the latest advances in AI, including predictive policy, face recognition, personalized diagnosis, early disease detection drug discovery, algorithmic decision-making used in courts, and government. We then identify what the main regulatory initiatives revolving around AI technologies in the European Union are, including the Ethics Guidelines from the European High-Level Expert Group on Artificial Intelligence. During the course, we will focus on the benefits but also the particular challenges revolving around the deployment of AI, including, transparency and explanation (following the European General Data Protection Regulation); potential discrimination scenarios and exacerbation of existing biases; the construction of responsibility in highly automated environments; and the blurring of well-established concepts such as safety, in the context of AI and interconnected products. We will explore solutions and also learn how to assess the risk posed by these technologies via the use of impact assessments. We close the course reflecting on the long-term consequences of AI, the added value this brings to society, and how the law should balance innovation and user rights protection in the context of AI.
Objectives of the course
The course Law and Artificial Intelligence has five main objectives:
Learn about emerging AI technologies, applications, and benefits.
Understand what the main challenges of AI are from the legal and regulatory perspectives.
Map the regulatory initiatives revolving around AI in Europe and the world.
Learn methodologies to assess the legal and ethical risks posed by AI.
Think critically about the deployment of AI in society.
Upon successful completion of this course, students will:
Understand the basic architecture and operation of AI and the directions in which it will develop in the near future.
Identify and recognize the potential benefits and drawbacks of the deployment of AI.
Learn and understand the fundamental regulatory issues that have emerged in relation to the deployment of AI, and the relevance of design choices in the architecture of AI.
Understand the complexity of the regulatory and policy landscape to address the legal and regulatory issues arising from the use of AI.
Learn practical methodologies to evaluate and mitigate the potential risks arising from the implementation of AI.
This knowledge should equip students with the ability to weigh and evaluate the development of specific AI applications, to see where potential regulatory and ethical challenges might arise in their use or deployment, and to learn methodologies to be able to give advice on designing AI technologies in a way that mitigates such issues.
The timetable of this course can be found here.
Mode of instruction
Number of (2 hour) lectures/seminars: 10
Names of lecturers: E. Fosch Villaronga and invited lectures.
Required preparation by students: Reading the required materials on Brightspace, submitting one assignment.
Interactive lectures for which attendance is mandatory and registration is required (in Usis the lectures are registered as working groups).
Group work (25%)
Final exam (65%)
Active participation in class (10%)
If students fail this course (weighted final grade < 5,5), the grade they have received for the assignment will no longer count for the retake! There won't be another assignment for the retake, the obtained points for the retake exam determine 100% of the final grade.
To be announced through Brightspace.
Areas to be tested within the exam
The examination syllabus consists of the required reading (literature) for the course, the course information guide and the subjects taught in the lectures, the seminars and all other instructions which are part of the course.
Obligatory course materials
Boden, M. A. (2018). Artificial intelligence: A very short introduction. Oxford University Press;
Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389-399;
High Level Expert Group on AI Ethical Guidelines;
Course information guide:
- See Brightspace.
- See Brightspace.
Recommended course materials
- See Brightspace.
Students have to register for the lectures and working groups through uSis. With this registration you have access to the digital learning environment of this course in Brightspace. You may register up to 5 calendar days before the first teaching session begins.
Students have to register for exams and retakes through uSis. With this registration you also have access to the digital learning environment of this course in Brightspace You may register up to 10 calendar days before the exam or retake.
Exchange students have priority and will be registered for the course first. Any remaining seats will be available for students from Leiden University and other Dutch Universities.
Coordinator: Eduard Fosch Villaronga
Work address: KOG A1.58
Telephone number: +31 71 527 2834
Institute: eLaw Center for Law and Digital Technologies
Department: Interdisciplinary Study of the Law (Metajuridica)
Room number secretary: B1.14
Telephone number secretary: 071 – 527 8838