CIFAR’s AI4Good National Training Program

Through the AI4Good National Training Program, CIFAR is training the next generation of AI leaders and advancing equity, diversity and inclusion in AI.

The CIFAR AI4Good National Training programs engage under-represented groups and/or develop AI-based products and services that deliver a positive societal benefit. Every year, CIFAR and its partners engage hundreds of Canadian and international students, from high school students to postdoctoral fellows, providing the opportunity to develop the skills, expertise and networks that they need to be successful in their future careers.

CIFAR Deep Learning + Reinforcement Learning Summer School (DLRL)


The DLRL Summer School provides 330 graduate and post-graduate trainees from across 20 countries with an opportunity to learn about the latest research, new developments and real-world applications of deep learning and reinforcement learning from some of the world’s leading researchers.  

The DLRL Summer School will run a virtual program from August 3-7, 2020.


Osmo AI4Good

Led by the Osmo Foundation in partnership with CIFAR, the AI4Good Lab promotes inclusive tech culture by empowering women in AI. The program targets undergraduate women enrolled in STEM programs and provides them with innovative approaches to teaching and learning AI. In February 2020, CIFAR and the OSMO Foundation, in partnership with Amii, announced the expansion of the Lab. The AI4Good Summer Labs take place in Montréal and Edmonton.

The AI4Good Summer Lab will run a virtual program from June 8 to July 28, 2020.

Invent the Future: AI Scholars Program

A two-week summer enrichment program for Grade 10 and 11 girls at Simon Fraser University’s Burnaby campus, sponsored by AI4ALL. 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.

Invent the Future will run a virtual program from July 13 to 24, 2020.

Data Science for Social Good (DSSG) Fellowship Program

This 14-week summer program at the University of British Columbia, sponsored by CIFAR, provides an interdisciplinary data science research experience for undergraduate students at UBC. Hosted by the Data Science Institute, DSSG Fellows will be divided into teams that 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 for urban-related topics.

IVADO International Summer School of Bias and Discrimination in AI

This program engages multidisciplinary teams of researchers and practitioners to explore the social and technical dimensions of bias, discrimination and fairness in machine learning and algorithm design. The course focuses specifically (although not exclusively) on gender, race and socioeconomic-based bias and data-driven predictive models leading to decisions.

Oxford Machine Learning Summer School

The Oxford Machine Learning Summer School 2020 aims to bring together some of the top talent in AI and medicine/healthcare with a goal to provide them with world-class training in Machine Learning / Deep Learning and Deep Medicine. It is a unique opportunity to learn about past, present and future of Machine Learning research in healthcare (from electronic health records, imaging, and genetics to wearable, drug discovery and more). The program is held in Oxford, UK, by Global Goals AI. CIFAR will support the participation of six Canadian postgraduate students in 2020.

The Oxford Machine Learning Summer School will run from August 17 to August 20, 2020.

Summer Institute on AI and Society

The Summer Institute on Artificial Intelligence Society, co-convened by CIFAR, the AI PULSE program at UCLA School of Law (funded by a generous grant from the Open Philanthropy project) and Amii, took place July 21-24, 2019 in Edmonton, Alberta, ahead of the DLRLSS. The program brought together a distinguished international group of 80 researchers, professionals and advanced students from a wide range of disciplines and areas of expertise for three days of mutual instruction and collaborative work on the societal implications of AI, machine learning and related technologies They examined issues such as AI in policing and criminal justice, predictive analytics in education and employment, facial recognition technology and many other relevant topics.