Advancing responsible AI adoption at scale: leadership and scope
As Canada’s AI leader, U of T is working to define what responsible, useful and trustworthy AI adoption looks like at institutional scale.
Commitment lead
Susan McCahan
Associate Vice-President and Vice-Provost, Digital Strategies
To meet the commitment for Advancing Responsible AI Adoption at Scale, we are identifying ways in which a large, human-centred institution like the University of Toronto can adopt artificial intelligence responsibly. This work occurs on multiple levels.
Governance and guidance
An AI Adoption Table of senior faculty and staff is developing guidelines and establishing where the boundaries sit. Its membership includes experts in data science, research compliance, libraries, teaching, student services and human resources. Faculty, librarians, staff and students have expressed that their comfort level in using these tools is low because the rules around usage are unclear. The AI Adoption Table exists to address this issue and many others like it. It seeks to advise on institutional policy and reports while consulting regularly with other groups within the university.
Infrastructure and access
The AI Kitchen provides tiered access to AI tools, so that people can use the technology at the level they need. Most of the U of T community requires trustworthy access to established models for teaching, learning, research and administration. Some need to build their own applications, and some conduct research that requires direct access to computing infrastructure. Through the university’s partnership with Cohere we are working to build a platform that allows people to work across multiple models and to develop their own tools with support from our staff. Working with a Canadian company like Cohere is part of how U of T ensures appropriate control and governance of its artificial intelligence ecosystem, including data, models, operations and infrastructure, ensuring alignment with local laws, security needs and institutional values.
Fluency across the community
AI resources for students, staff and faculty are already in place and heavily used. Some of these resources include AI literacy workshops, self-directed modules, an AI tutoring system deployed by faculty in select courses and communities of practice across the three campuses. Over the coming year we will organize AI materials and resources into shorter, more accessible paths built for specific groups, so that a business officer, a librarian or a faculty member in any given discipline has a clear place to start.
The campus as a living lab
The University of Toronto used this approach to advance its sustainability efforts, pairing large institutional projects with research insights from our faculty and student engagement opportunities across our campuses. As an in-person, human-centred organization, we can demonstrate how large, complex institutions can use AI in ways that align with human interests. This means tackling challenging questions about the use of AI and addressing environmental, economic, and governance concerns while we develop solutions to practical institutional use cases. We will evaluate what we build, publish what we learn and share practical guidance with government, industry and other institutions.
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