Montreal calls itself an AI hub, but a hub for whom?

Gender gaps in AI shape who benefits and who gets erased

OpinionsMani Asadieraghi — Published October 20, 2025 0 minutes

The world hails Montreal as an AI leader. Yet the people designing its technologies don’t reflect the diversity of the society they impact. Women, gender-diverse individuals and people of colour remain underrepresented. That isn’t a superficial gap—it’s structural, determining who designs technology and whose experiences become excluded from the algorithms that shape daily life.

Globally, women make up just 22 per cent of AI professionals according to a 2018 World Economic Forum report. Yet even this figure masks deeper exclusions: most statistics treat gender as binary and rarely account for the experiences of non-binary individuals. Additionally, even this already low figure overlooks other dimensions of diversity: women with disabilities, Indigenous women and women of colour, all of whom face even greater marginalization in AI. 

Locally, the gap shows up in classrooms, labs and companies across the city of Montreal. We can see the consequences everywhere: hiring algorithms that rank male candidates as more desirable, medical tools that underdiagnose non-male patients, and facial recognition systems that misidentify racialized women at alarming rates.

When homogenous teams design “intelligent” systems, those systems inevitably inherit their blind spots.

That’s why gender diversity in AI isn’t just about fairness. It’s about power. Diverse teams don’t just change who sits at the table—they change what gets noticed. Varied perspectives are better at spotting missing information, drawing on lived experience, and anticipating impacts that homogeneous groups routinely overlook. 

Too often, technology assumes the identity of its “imagined user” as able-bodied, male and white. Teams that include women, non-binary, Indigenous and racialized voices can more clearly address those blind spots—and are more capable of designing systems that work for everyone. 

If Montreal wants to claim global leadership in AI, it has to lead on this question too. Otherwise, the city simply exports inequities through the technologies it builds.

Some institutions have begun to respond. At Concordia University, the Applied AI Institute has made equity and justice one of its core pillars. Its mentoring program, GEMinAI, pairs women and non-binary students with professionals across academia and industry—carving pathways into spaces that too often push them out. 

Another Concordia initiative, Affecting Machines, applies feminist perspectives to AI research, challenging the myth of technological neutrality and rethinking the structure of lab cultures. 

Abundant Intelligences, co-directed at Concordia and Massey University, takes this further by building AI systems grounded in Indigenous knowledge, designed for abundance rather than scarcity and for lifeways that sustain future generations.

And in Concordia's Milieux Institute for Arts, Culture and Technology, Machine Agencies treat AI not as a race for speed but as a cultural craft—emphasizing endurance over immediacy, and play over simulation.

Each of these projects shows what becomes possible when we imagine technology from multiple vantage points, not just one.

Still, such programs remain the exception, not the rule. Montreal loves to tout its breakthroughs and investments, but rarely asks who gets left out of the story. Unless we build equity into AI’s foundations, the field will keep replicating old hierarchies with new tools.

And this matters far beyond classrooms or labs. AI is creeping into workplaces, hospitals and governments. Who builds it will decide whether these systems entrench discrimination or dismantle it. Without diverse perspectives, we arrive at a predictable result: technologies that claim to be “intelligent” but only serve a few.

Closing the gender gap in AI isn’t charity—it’s survival. Programs like GEMinAI, Affecting Machines, Abundant Intelligences and Machine Agencies prove that change can occur—but they also reveal how much more work remains.

Universities, industry and government must stop treating diversity as a checkbox and start treating it as a precondition for trustworthy technology. Sustained funding, cultural change and accountability aren’t optional if Montreal wants its AI leadership to hold meaning.

The city has the chance to set a global standard, but not if it builds its reputation on exclusion. A hub that ignores diversity isn’t a hub at all. 

Because the real issue isn’t just who gets to study AI, it’s who gets to define intelligence and, in turn, who gets to define us.