Canada's ambitious "AI for All" strategy risks reproducing the racial inequalities that AI systems have historically amplified, according to critics who point out that the 27-member task force developing the plan includes no Black representation. The omission raises urgent questions about who gets to shape technologies that increasingly determine access to healthcare, employment, and justice in the country.

Why Does Black Representation Matter in AI Governance?

When Prime Minister Mark Carney and Minister of Artificial Intelligence and Digital Innovation Evan Solomon unveiled Canada's National Artificial Intelligence Strategy earlier this month, they framed it around a promise of inclusivity. But that promise rings hollow to many observers, particularly those from communities that have borne the brunt of algorithmic bias.

The stakes are concrete and measurable. Facial recognition technology, a tool increasingly deployed for security and identity verification, performs dramatically differently across racial groups. Research cited by The Washington Post found that African American individuals were up to 100 times more likely to be misidentified than white men, while Indigenous communities experienced some of the highest false-positive rates. In healthcare, the consequences can be life-or-death. A race-based algorithm used in kidney transplant eligibility delayed treatment for roughly 14,000 Black candidates by overestimating their kidney function, according to reporting on a case involving Jazmin Evans, a Black woman who waited four years for a transplant before discovering the bias.

The absence of Black voices from Canada's AI governance structure is not merely a symbolic oversight. In October 2025, 60 Black Canadian scholars signed a joint letter calling on the federal government to address the lack of Black representation on the task force. Their concern reflects a fundamental principle: representation in this context is expertise, not symbolism.

What Are the Real-World Harms of Biased AI Systems?

AI systems do not emerge in a vacuum. They are built on historical data, institutional practices, and social structures that reflect existing racial inequalities. Many AI systems are trained on datasets that have historically overrepresented white faces and underrepresented Black and other racialized communities. As these systems become more widely adopted without input from affected communities, the consequences become far more significant.

The speed of AI adoption in Canada underscores the urgency. In 2025, Statistics Canada reported that 21.5% of businesses in the professional, scientific, and technical services sector considered investment in AI to be very important, highlighting how rapidly these technologies are being integrated into everyday decision-making.

• Surveillance and Policing: Facial recognition systems misidentify Black individuals at dramatically higher rates than white individuals, leading to wrongful arrests and increased police scrutiny of Black communities.
• Healthcare Access: Race-based algorithms in medical systems have delayed treatment for thousands of Black patients by producing inaccurate health assessments, as demonstrated by the kidney transplant eligibility case.
• Employment and Credit: AI hiring and lending algorithms trained on biased historical data can perpetuate discrimination in hiring decisions and credit access, limiting economic opportunity.

How Can Canada Address the Representation Gap?

Experts and advocates have outlined concrete steps the Canadian government should take to ensure that AI governance reflects the communities most vulnerable to algorithmic harm. These recommendations focus on both immediate and long-term institutional changes.

• Establish an Equity Subcommittee: Create a dedicated Equity and Accountability Subcommittee composed of Black and other racialized researchers, entrepreneurs, and community advocates with an explicit mandate to address anti-Black racism and AI adoption.
• Invest in Black-Led Research: Fund Black-led AI research, data sovereignty initiatives, and community remediation programs to develop culturally grounded approaches to AI governance aligned with Canada's Anti-Racism Strategy.
• Center Community Voice in Decision-Making: Ensure that Black communities are not an afterthought but central to the decision-making bodies governing AI adoption, particularly technologies that determine who is monitored, who receives critical services, and whose voices are heard.

The stakes extend beyond equity concerns. Canada's AI strategy aims to generate $200 billion in economic growth and create 250,000 AI-related jobs. Without equitable participation in governance, these benefits and risks will not be distributed evenly. Technological progress without inclusive decision-making risks concentrating economic gains among those already positioned to succeed while leaving marginalized communities to shoulder the consequences.

"The future of AI should not be something done to Black communities; it must be something built with them," stated Hermon Afowork, the author of the analysis.

Hermon Afowork, Policy Analyst

The question now is whether the federal government will rise to the challenge. If "AI for All" truly means all Canadians, then Black communities cannot remain an afterthought. The decisions made in the coming months about how Canada develops and deploys AI will shape whose interests are protected and whose vulnerabilities are ignored for years to come.