Teaching Data Driven Law

This February (2018) LSTS prof. Mireille Hildebrandt will teach an ‘intensive course’ at University of Toronto, Faculty of Law on Data Driven Law. The course will build on her explorations of the entanglements between law and smart technologies, inquiring further into the impact of data-driven environments on democracy, law and the Rule of Law.

The course will investigate machine learning (ML) as a novel way to detect, identify and construct knowledge, taking ML seriously as a scientific discipline with its own methodological loyalties and its own specific requirements of methodological integrity. On the menu will be, amongst others, law as information, the relationship between law, democracy and the Rule of Law, ML and data protection law, issues of criminal law, jurisdiction and law as computation under the Rule of Law. 3624892015424b83f37ce7b47d29e99b

Core questions will be: how shall we move beyond the assumed trade-off between ML accuracy and ML interpretability? which characteristics of the Rule of Law must be reinvented in a data driven environment? how does the capture of behavioural data transform our autonomy? should we admit to eschatological expectations of deep learning? or should we? what does this mean for the trade of lawyers? and what does it mean for their profession? how does automation of decisions in the realm of criminal law affect core principles such as the presumption of innocence? will distributed ledger technologies ‘freeze the future’ or will they set us free? how will the regulatory state transform in the era of data driven anyware? shall we figure out ways and means to embed legal and democratic values in the architecture of artificial legal intelligence? And much more.

 

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