Unlocking Inclusion: The Key to Canada’s Science and Innovation Future

When I think about the future of science and innovation in Canada, I feel both optimism and unease. Optimism for what’s possible, and unease about who continues to be excluded. Behind every potential breakthrough lies a difficult but important truth: not everyone gets to participate, contribute or benefit. Across the country, many organizations and individuals are working to change that by building skills, opening doors and creating space for women, Indigenous people, people from racialized communities, people with disabilities, and those in the 2SLGBTQ+ community. The progress is inspiring and necessary, but the road ahead is still long.

What’s holding back Canada’s science and innovation system from moving faster and further? The answer, at least in part, lies in the systems themselves.

At Actua, I’ve seen how broken systems and entrenched, outdated culture can block the path forward. After engaging millions of youth, educators and their parents over three decades, I can say with certainty that it isn’t talent or motivation holding people back. It’s the deeply rooted norms, policies and behaviours that too often go unquestioned. The result is that Canada misses out on countless brilliant ideas and talents. We shouldn’t be asking anyone to fit into systems and cultures that aren’t serving everyone. Real progress comes when institutions and workplaces evolve to work for all, rather than expecting individuals to bend themselves to fit the system.

What AI Reveals About the Need for Inclusive Systems

Artificial intelligence, especially generative AI, is a clear and current example of why systemic change in science, technology and innovation is needed. At Actua, I’ve witnessed the enthusiasm of youth and educators as they explore AI. But alongside it, I also see the persistence of biases that, if left unchallenged, risk being amplified by the very tools we’re celebrating.

Consider many women’s experiences with AI. Research from Harvard Business School highlights a gap many may not have realized: women are about 25 percent less likely to use generative AI than men, with the gap climbing as high as 40 percent in some industries, roles and countries. Too often, the conversation about women’s lower uptake of a particular technology is framed in terms of confidence or comfort. But these gaps reflect structural issues like who these tools are built for, who feels welcome using them and whose voices were included when they were designed.

Another study makes this even clearer. Researchers asked large language models (LLMs) for salary negotiation advice, submitting identical resumes labeled as men’s or women’s. The results were stark. Women were advised to request much lower salaries, sometimes nearly $100,000 less. Troubling, but not surprising, since these models are trained on data that reflect long-standing inequalities instead of challenging them. This example shows that AI doesn’t just mirror existing biases; it can reinforce and advance them with often devastating consequences.

With women making up just 22 percent of the global AI workforce and less than 14 percent of senior executive roles, it’s no wonder these biases go unchecked. When multiple perspectives are absent from science and innovation, exclusion continues to be built directly into the systems we all depend on.

Women’s lower use of generative AI illustrates how technologies, workplaces and industries can fail to account for their realities. Systemic change matters, not just to increase use but to ensure diverse voices shape AI from the very start in classrooms, labs, design teams and boardrooms. Only then can technologies truly work for everyone.

Rebuilding Science & Innovation for Everyone

The challenges we face in developing and using AI are the same ones across Canada’s science ecosystem. We can’t expect people to fit into systems that weren’t designed for them and the real work is transforming those systems.

That means breaking down barriers wherever we can, whether through travel bursaries, scholarships, or bringing science and technology programs to rural and remote communities. It also means rethinking how schools and workplaces function by providing mentoring, sponsorships, fair pay, inclusive curricula and spaces where people can show up as their authentic selves. At a time when some of these efforts are at risk, we should be pushing forward, not stepping back.

Building trust in science is just as important. People need clear communication, opportunities to learn and engage, and to see scientists as part of their communities, especially now, when misinformation spreads so easily. National science and technology plans should expand hands-on science and technology learning for underrepresented youth, both in and out of the classroom, and recognize knowledge from a variety of sources, including Indigenous Knowledge in areas like medicine, engineering and conservation.

By rethinking who our systems serve, Canada can lead the world in building a bold, inclusive, and future-ready science culture. When everyone has a chance to contribute, we don’t just open doors for individuals, we strengthen our capacity for discovery, innovation and long-term competitiveness.

This won’t happen by accident. While we continue helping individuals build skills, we also need to confront the structures that decide who gets to belong and who succeeds.

Canada’s full potential lies in the creativity and determination of all its people. Real progress will come when our institutions and culture are designed not just to welcome that diversity, but to rely on it. I believe we can get there and I’ve seen enough change already to know that a stronger, more inclusive future is within our reach.

Actua is a leading science, technology, engineering and mathematics (STEM) outreach organization reaching 500,000 youth in 600 communities each year. For more information about Actua, visit actua.ca.

1.  Otis, Nicholas G., Solène Delecourt, Katelynn Cranney, and Rembrand Koning. “Global Evidence on Gender Gaps and Generative AI.” Harvard Business School Working Paper, No. 25-023, October 2024. (Revised January 2025.)
2.  A. Sorokovikova et al. “Surface Fairness, Deep Bias: A Comparative Study of Bias in Language Models.” arXiv June 2025.
3.  S. Pal, R. Lazzaroni and P. Mendoza. “AI’s Missing Link: The Gender Gap in the Talent Pool.” October 2024.