Vector partners with IBET to increase the number of Indigenous and Black AI researchers

October 17, 2023

News Partnership

The Vector Institute has partnered with the Indigenous and Black Engineering and Technology PhD Project (IBET) to increase the number of Indigenous and black professors in engineering and computer science in Canada. 

Through the partnership, IBET fellows will gain access to Vector programming, including research talks and courses, as well as the Vector Digital Talent Hub, where they can connect with leading industry sponsors on AI-related internships and work opportunities.  

“We are delighted to be partnering with the IBET PhD Project and welcome the participation of IBET fellows to our programming” says Melissa Judd, Vice President of Research Operations and Academic Partnerships at Vector. “The partnership is an important part of Vector’s commitment to increase the participation of Black and Indigenous researchers and practitioners in AI.”

Removing barriers for Indigenous and Black students

IBET, formed in 2021 when the Faculties of Engineering at six Ontario Universities came together to help reduce the systemic barriers that exist for junior Indigenous and Black scholars pursuing doctoral degrees in engineering and STEM programs. By fostering equitable and inclusive research environments, IBET helps to alleviate these barriers making it easier for Indigenous and Black students pursuing PhDs. Since its launch, 10 more engineering faculties have joined the Project consortium, with an end goal of increasing the number of Indigenous and Black faculty in universities across Canada. 

The new partnership formalizes an already established relationship between IBET and Vector. 10 IBET fellows will take part in Vector’s Excel to AI course for Black and Indigenous post-secondary students in September 2023. An additional three IBET fellows — Patrick Adjei, Mai Ali, and Stephen Obadinma — are currently working with Vector. 

Helping Indigenous and Black students thrive

After taking Vector’s Introduction to Machine Learning course last winter, Adjei, a Ph.D. student at Western University working in the reinforcement learning field, worked with Vector Faculty Members Amir-massoud Farahmand and Juan Felipe Carrasquilla Álvarez as part of Vector’s Black and Indigenous Research Internship. “IBET will contribute to making [people] aware [of] the creative abilities of blacks and Indigenous people overall,” he says of the project. “This will manifest itself in the AI ecosystem across the country for good.” 

Along with her interest in applying machine learning to health, Ali, a PhD at University of Toronto, founded the non-profit, AI1010, through which she teaches AI & ML to students in Africa. “By providing support, resources, and opportunities to Indigenous and Black individuals in engineering and technology fields, [IBET]  can help bridge existing gaps in representation,” she says. “This fellowship can lead to a more diverse and inclusive AI community, fostering a broader range of perspectives and ideas, ultimately enriching the Canadian AI ecosystem and driving innovation from underrepresented voices.”

Blob

“This fellowship can lead to a more diverse and inclusive AI community, fostering a broader range of perspectives and ideas, ultimately enriching the Canadian AI ecosystem and driving innovation from underrepresented voices.”

Mai Ali

PhD Student at University of Toronto

Obadinma, currently working as an Applied ML Technical Associate with Vector’s Health and AI Engineering teams, agrees. “With Vector’s support, IBET has the potential to make Canada one of the first places in the world where Black and Indigenous researchers can thrive in AI. 

“A lot of concerns have been brought up nowadays with AI models that can be biased against certain groups,” says Obadinma, a PhD student at the department of Electrical and Computer Engineering at Queen’s University, where he is a member of Ingenuity Labs. “It is important to get people from communities most affected to be involved in the process of developing these models so that these concerns are better addressed.” 

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