Taking Responsibility for Responsible AI
On August 26, Join the Vector Institute and the Schwartz Reisman Institute for Technology and Society hosted Chris Meserole of the Brookings Institution to explore opportunities to collaborate on advancing the Vector Institute’s vision to use AI to foster economic growth and improve the lives of Canadians.
Canada was the first country to announce a national AI strategy in 2017. The world quickly followed suit. The same year, China announced its intent to become the world’s AI leader by 2030 and shortly after, the United States launched the American AI Initiative.
Recognizing the transformative potential of AI, organizations are evolving to handle complex interdisciplinary questions. MIT launched the Schwarzman College and Stanford established the Institute for Human-Centered Artificial Intelligence (HAI). Companies are hiring ethicists, developing ethical AI principles, and investing in related initiatives.
Right here in Toronto, the largest ever donation to the University of Toronto will establish the Schwartz Reisman Institute for Technology and Society to harness strengths across disciplines. And governments are exploring digital governance principles.
Canada’s concentration of world-class machine learning scientists presents a major opportunity to turn knowledge into economic competitiveness. What will the roles of governments and businesses be in maximizing Canada’s economic opportunity while protecting Canadians’ rights and values? Will we be leaders or laggards?
Gillian Hadfield: Faculty Affiliate, Vector Institute; Inaugural Director, Schwartz Reisman Institute for Technology and Society and Schwartz Reisman Chair in Technology and Society; Professor of Law and Strategic Management at the University of Toronto Rotman School of Management
Topic: Overview: Schwartz-Reisman Institute for Technology and Society
Graham Taylor: Canada CIFAR AI Chair and Faculty Member, Vector Institute; Academic Director, University of Guelph Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI); Associate Professor and Canada Research Chair in Machine Learning, School of Engineering, University of Guelph; Visiting Faculty, Google Brain Montreal (until 2019-05); Academic Director, NextAI
Topic: Overview: University of Guelph Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI)
David Madras: PhD Student in the Machine Learning Group at the University of Toronto and the Vector Institute
Topic: Machine learning in decision-making systems
Aleksandar Nikolov: Professor, Department of Computer Science, University of Toronto; Canada Research Chair in Algorithms and Private Data Analysis
Topic: Differential Privacy: Rigorously Private Data Analysis
Sheila McIlraith: Faculty Affiliate, Vector Institute; Professor, Department of Computer Science, University of Toronto
Topic: AI Safety
Melissa McCradden: Postdoctoral Fellow in Ethics of AI in Healthcare, Department of Bioethics and Genetics & Genome Biology at The Hospital for Sick Children and Vector Institute
Research interests: Translation of AI tools into clinical care, neuroethics, paediatric bioethics, and sport ethics
Shalmali Joshi: Postdoctoral Fellow, Vector Institute
Topic: Towards safe deployment of (clinical) machine learning models
Frank Rudzicz: Canada CIFAR AI Chair and Faculty Member, Vector Institute; Director of AI, Surgical Safety Technologies Incorporated; Scientist, International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St Michael’s Hospital; Associate Professor, Department of Computer Science, University of Toronto
Topic: AI safety, explainable AI in the operating room, standards for evaluating ML models
Charles Morgan: President, International Technology Law Association (ITechLaw); Partner, McCarthy Tétrault
Topic: ITech Law’s recently published “Responsible AI: A Global Policy Framework”
Chris Meserole: Fellow in Foreign Policy at the Brookings Institution and Deputy Director of the Brookings Artificial Intelligence and Emerging Technology Initiative
Advancing AI: A Framework and Agenda for Cross-Disciplinary Research
Abstract: Technical breakthroughs in deep learning architectures, such as capsule networks and transformers, continue to drive forward the state-of-the-art in AI performance. Likewise, research on the ethical, legal, and social implications (ELSI) of AI has also advanced rapidly in recent years, most notably the work on ethical principles. Yet most AI scholarship remains relatively siloed, despite the urgent need for work informed by both technical and non-technical fields. This talk aims to sketch a brief framework and agenda for cross-disciplinary research on AI.