Equity, Diversity & Inclusion in AI
CIFAR and our partners are committed to advancing equity, diversity and inclusion in the Canadian AI ecosystem and globally, to ensure that AI is developed and deployed to the benefit of all citizens of the world.
CIFAR's EDI Action Plan
Indigenous Perspectives in AI
This free certificate-granting course teaches AI trainees about Indigenous knowledge-keeping and worldviews. Self-paced for independent learning, it takes about 2.5 hours to complete online.
The modules are offered at three levels of education (high school, undergraduate, and postgraduate) in both French and English. A course guide is also available for instructors who wish to integrate it into their own curricula.
The course was co-authored and co-constructed by a team of Indigenous and non-Indigenous educators, facilitated by Ridge Road Inc and commissioned by CIFAR.
Are you an instructor who wants to offer this course to your students?
Email us at ai@cifar.ca and we'll send you the course guide.
CIFAR NextGen AI Training Programs
Each year, CIFAR and its partners provide training opportunities to hundreds of Canadian and international students — from high school students to postdoctoral fellows — to help them develop the skills, expertise and networks they need to be successful in their future careers.
These programs are dedicated to engaging equity-deserving groups in AI, and supporting programs that advance AI approaches for positive societal benefits.
CIFAR Inclusive AI Scholarship Program
To lower barriers to participation of Black and Indigenous students in CIFAR’s partnered AI training programs, CIFAR is pleased to support the full costs of participation of self-identified Black and Indigenous (First Nations, Inuit, or Métis) students in AI training programs delivered by our current NextGen AI Training Program partners listed below.
Information about how to apply for this scholarship is available in the application forms for the partner programs.
Advanced Machine Learning Training Network (UBC)
For graduate trainees
AML-TN is an advanced machine learning research training program with value-added training opportunities beyond what traditional machine learning graduate programs provide. Foremost amongst those is a funded research mobility program which enables students to travel to and have hosted research stays at one or more world-class institutions in their global network of research mobility partners. To apply to join AMLTN you must either already be a UBC graduate student or you must first apply and be admitted to a research graduate program in one of the Computer Science, Statistics, or Electrical and Computer Engineering departments at UBC.
AI4Good Lab
For undergraduate trainees
A 7-week summer training program that brings together 90+ undergraduate trainees from across Canada who identify as women and other gender minorities in STEM. The program is offered in Edmonton, Toronto and Montreal and includes lectures, workshops and self-directed team projects. Instructors and mentors include university researchers, graduate students, and practitioners from startups, corporates, intergovernmental organizations, and city government. The program emphasizes curiosity-driven learning and mentorship both from peers and industry experts.
CIFAR Deep Learning + Reinforcement Learning Summer School (DLRL)
For graduate and postdoctoral trainees
An annual international opportunity, providing cutting-edge training on the real-world applications of deep learning and reinforcement learning from some of the world's leading researchers. We welcome students from all around the world, with a focus on inclusion of those who identify as members of underrepresented groups in AI.
Indigenous Pathfinder in AI Program (Mila)
For Indigenous undergraduate and graduate trainees
Delivered in partnership with Indspire, Indigenous Pathfinders in AI is a career pathway program designed to inspire Indigenous talent to learn, develop and lead the evolution of artificial intelligence, shaping a future marked by their contributions.
Invent the Future:
AI Scholars Program
For high school students
A two-week summer enrichment program for Grade 10 and 11 girls delivered by Simon Fraser University, sponsored by AI4ALL and CIFAR. Participants explore the world of AI through team projects, industry field trips, and by connecting with mentors and industry experts in supportive yet challenging environments.
Data Science for Social Good (DSSG) Fellowship Program
For graduate trainees
This 14-week summer program at the University of British Columbia's Data Science Institute provides an interdisciplinary data science research experience for graduate students. Multi-disciplinary teams work on research projects submitted by local governments or non-profits. These projects involve analyzing multiple data sets and have an applied social good component.
Let's SOLVE it
For undergraduate trainees
In partnership with RBC and Borealis AI, CIFAR supports this mentorship program that provides undergraduate students from diverse backgrounds with mentorship, contacts, training and the guidance they need to make their project a reality. Applicants require a good idea, a team of peers, a strong sense of curiosity, and commitment to making a difference.
Responsible AI (TMU)
For undergraduate and graduate trainees
Hosted by Toronto Metropolitan University (TMU), Responsible Artificial Intelligence is a six-year multidisciplinary, multi-sector training initiative to build sustainable connections, research, training and knowledge capacity and a pipeline of highly qualified trainees in Canada’s fastest-growing knowledge economy sector. Students must be enrolled in Toronto Metropolitan University, Western University, McGill University, University of Quebec, University of Montreal, University of Waterloo, or University of Calgary.
Summer School in Responsible AI and Human Rights (Mila)
For graduate, early-career and professional trainees
This joint initiative by Mila and the Université de Montréal brings together participants from different backgrounds and horizons to explore the intersections between responsible artificial intelligence and human rights.
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