Instructor(s): Gillian Hadfield

This section of the course is for students studying remotely. This course will meet once a week remotely, via audio-visual conference. There will also be some asynchronous on-line components. To enrol in this course all students must meet or exceed the tech requirements for enrolment in University of Toronto courses, which can be found here []

Note: The Quercus program will be used for this course. Students must self-enrol in Quercus as soon as confirmed in the course in order to obtain course information.

This course will explore the challenges around building effective legal and regulatory systems to govern the development and deployment of artificial intelligence (AI) throughout society. We will begin with an overview of the ways in which AI is or soon will be deployed throughout society. We’ll then dive into understanding some of the nuts and bolts of how machine learning (ML) works and why ML presents special challenges for law and regulation. We’ll explore a few concrete settings such as the use of AI in the legal system, healthcare, and social media, looking at the nature of the risks presented and the limits of existing governance mechanisms. We’ll also explore ideas for innovative approaches to governance challenges. Final reports will be based on choosing a particular AI application, articulating the risks of the application, and evaluating and/or proposing governance approaches to address these risks.

40% class participation (10% class contributions; 15% discussion leadership; and 15% on the formulation of discussion questions ahead of class), and 60% final paper of 6,250 to 7,500 words.
Academic year
2020 - 2021

At a Glance

Second term



23  JD


T: 9:30 - 11:00 am (AV Conference)