B9770 - Sem. Artificial Intelligence and Criminal Justice

Learning outcomes

This seminar will explore the challenges posed by artificial intelligence (AI) to the realm of criminal justice. Specifically, it will focus on whether AI poses specific issues to the attribution of criminal liability and whether we can talk of an accountability and/or liability gap (who should be blamed when an AI system goes wrong?). The students will have the opportunity to reflect on classical notions of criminal liability and assess whether they can be applied to such technology. The seminar will also touch upon the most recent European and international legislative efforts (e.g., the AI act) on this topic and give students the tools to analyze them critically.

Course contents

1. Artificial intelligence and criminal liability: gaps and challenges

2. Artificial intelligence and criminal liability: case studies

Readings/Bibliography

- B.H.M. Custers, ‘AI in Criminal Law: An Overview of AI Applications in Substantive and Procedural Criminal Law’, in: B.H.M. Custers & E. Fosch Villaronga (eds.), Law and Artificial Intelligence, Springer (2022).

- Alice Giannini, ‘Artificial Intelligence, Criminal Liability for’, Encyclopedia of the Philosophy of Law and Social Philosophy (2023).

- Alexander Sarch & Ryan Abbott, ‘Punishing Artificial Intelligence: Legal Fiction or Science Fiction’, UC Davis Law Review 53:323-384 (2019).

- Sabine Gleß, Emily Silverman and Thomas Weigend, ‘If Robots Cause Harm, Who Is to Blame? Self-Driving Cars and Criminal Liability’, New Criminal Law Review 19 no. 3 (2019).

- Ying Hu, ‘Robot Criminals’, 52 U. Mich. J. L. Reform 487 (2019).

- Kamil Mamak, ‘Should criminal law protect love relation with robots?’, AI & Society, Vol. 39 (2024).

Teaching methods

The seminar will be held in two parts: in the first part, general aspects and challenges of attributing criminal liability when AI is involved will be addressed. In the second part, the seminar will focus on case studies, such as current legislation addressing AI and criminal liability and real-life cases involving AI and criminal law. During the first class, students will be divided into groups. The groups will be asked to present their analysis of a case study in the second class.

Students are expected to come prepared to the lectures and will be involved in an interactive and open discussion.

Assessment methods

An evaluation will be made at the end of the seminar based on participation and comments during the discussions.

Office hours

See the website of Emanuela Fronza