David Duvenaud and Richard Zemel are recognized for their innovative AI research in areas that benefit society. The CIFAR AI Chairs program is a cornerstone of the CIFAR Pan-Canadian AI Strategy, established in 2017 to recruit the world’s leading AI researchers to Canada and retain existing talent. With today’s announcement, the number of CIFAR AI Chairs surpasses 100.

While Canada’s proposed Bill C-11 offers a definition of deidentified data, computer scientists Sasho Nikolov and Nicolas Papernot argue this is inadequate because it focuses on modifying personal information rather than method of analysis. They offer an accessible explanation of privacy protection frameworks centered around algorithms rather than the data themselves.

Canada’s proposed Bill C-11 requires “knowledge and consent” for the collection, use, or disclosure of personal information. However, Bill C-11 also creates some new exceptions to consent, including for personal information that has been “de-identified.” In this article, experts Lisa Austin and David Lie examine this new de-identification regime.

The Canadian government’s proposed new private sector privacy legislation (Bill C-11) introduces some notable changes to existing legislation (PIPEDA), including new exceptions to the “knowledge and consent” requirements. Schwartz Reisman Research Lead Lisa Austin, specialist in law and technology, comments on the “business activities” exception.

Privacy law reforms are cropping up around the globe, as data and new technologies become increasingly powerful, autonomous, and ubiquitous. What are the details of the proposed new legislation in Canada and others like it around the world? And what are the implications for citizens, technologists, businesses, and public policy? A new series explores these questions.

As the first generation to grow up with the internet, millennials are more likely to be well-versed in digital technology. New research by Kristina McElheran shows that older workers who struggle with software may lose out when software investment in firms increases. Learn more about this detailed analysis of over a decade of U.S. Census Bureau data. 

AI tools and technologies are steadily being integrated into all aspects of our lives. While AI’s potential benefits are immense, the harms they can cause have led to a lack of trust. A new partnership between AI Global and the Schwartz Reisman Institute, in collaboration with the World Economic Forum, will create a globally recognized certification mark for the responsible and trusted use of AI systems.

In a digital and increasingly data-rich world, legislation is struggling to keep up with the ways in which data is generated, circulated, and used. Recently, the Ontario government sought formal responses from organizations housing legal or technical experts on issues surrounding data and privacy. Read what the Schwartz Reisman Institute’s experts had to say.

Meet the Schwartz Reisman inaugural group of faculty and graduate fellows, whose areas of research and proposed projects vary widely in scope and subject matter but are united in their focus on ensuring powerful new technologies are effective, safe, ethical, and fair. SRI is pleased to support the innovative work conducted by these stellar U of T researchers across all three campuses.

Deborah Hellman, professor of law at the University of Virginia, spoke at the Schwartz Reisman Institute’s weekly seminar about the ways in which algorithmic decision-making can exacerbate the already-present possibility of “compounding injustice” and “accuracy-affecting injustice.” To capture our moral intuitions in cases like this, Hellman proposes the “Anti-Compounding Injustice Principle.”

SRI Director Gillian K. Hadfield will discuss her book Rules for a Flat World as part of Rotman’s Big Ideas series. The paperback edition includes a new prologue about artificial intelligence—its risks, benefits, evolution, and regulation. In this interview, Hadfield offers insights into how we might understand, govern, and build technology that is responsive to human values.

From broadening not only the scope of data used by researchers to expanding conceptions of what data is—and can be—Schwartz Reisman Faculty Affiliate Marlène Koffi makes three recommendations for social science researchers who want to leverage powerful new technologies like machine learning in their work.