
SRI Seminar Series continues in fall 2025 with leading voices on technology and society
The Schwartz Reisman Institute is proud to announce its SRI Seminar Series programming for Fall 2025. This semester, an extraordinary lineup of scholars, technologists, and legal thinkers will examine urgent issues at the intersection of technology, governance, and society. Through thought-provoking presentations of new research and ideas, the series will explore topics ranging from the ethics of artificial intelligence (AI) in education and the creative industries, to the governance of advanced AI systems, to the social and political dynamics of democratic engagement in the digital age.
Schwartz Reisman Institute welcomes 24 new faculty affiliates for 2025–26
The Schwartz Reisman Institute for Technology and Society (SRI) is pleased to announce the appointment of 24 new faculty affiliates for the 2025–26 academic year—its largest incoming cohort to date. Representing a broad range of disciplinary backgrounds, the new affiliates bring deep expertise in artificial intelligence (AI), data science, public policy, digital culture, healthcare, ethics, law, sustainability, and education.
University of Toronto team discovers vulnerability at hardware-software boundary in cloud systems
David Lie, director of SRI, Gururaj Saileshwar, assistant professor in the Department of Computer Science, and Yuqin Yan a student at the Department of Electrical & Computer Engineering, discovered a security flaw in AMD’s cloud protection technology, revealing how interactions between hardware and software can expose sensitive data.
Navigating trust in AI: Call for expressions of interest
SRI Research Lead Beth Coleman is leading a transdisciplinary working group focused on trust in AI design and governance, which will convene during the 2025-2026 academic year. Applicants are encouraged to submit expressions of interest to participate, with a deadline of August 15, 2025.
Absolutely Interdisciplinary 2025 explores new frontiers in AI research
At SRI’s annual conference, participants discussed future directions and key challenges in artificial intelligence (AI) research, including the complexities of aligning advanced AI with human values and interdisciplinary perspectives on AI safety.
Schwartz Reisman Institute announces 2025–26 graduate fellows
The Schwartz Reisman Institute for Technology and Society is proud to announce the appointment of fifteen new graduate fellows from across the University of Toronto.
Call for proposals for 2025–26 SRI research leads now open
The Schwartz Reisman Institute for Technology and Society has launched its call for research leads. Open to University of Toronto faculty with a continuing tenure stream appointment, applications are due June 8, 2025.
SRI graduate fellows explore the evolution of genomic language models
Can AI unlock the hidden rules of our DNA and revolutionize medicine? SRI Graduate Fellows Micaela Elisa Consens and Ben Li explore this question in a new commentary examining the potential of genomic language models to transform biomedical research.
AI agents pose new governance challenges
How do we successfully govern AI systems that can act autonomously online, making decisions with minimal human oversight? SRI Faculty Affiliate Noam Kolt explores this challenge, highlighting the rise of AI agents, their risks, and the urgent need for transparency, safety testing, and regulatory oversight.
Schwartz Reisman Institute leaders join Canada's push for safe AI
SRI Director David Lie and Co-Chair David Duvenaud were appointed to Canada’s new Safe & Secure AI Advisory Group. Their expertise will help shape policies at this crucial time in AI's development and contribute to Canada's efforts to keep these powerful technologies safe.
Absolutely Interdisciplinary returns this spring to explore new frontiers in AI research
The Schwartz Reisman Institute’s annual academic conference Absolutely Interdisciplinary returns for 2025 to explore interdisciplinary approaches to AI governance, risk and safety.
Unequal outcomes: Tackling bias in clinical AI models
A new study by SRI Graduate Affiliate Michael Colacci sheds light on the frequency of biased outcomes when machine learning algorithms are used in healthcare contexts, advocating for more comprehensive and standardized approaches to evaluating bias in clinical AI.