Thanks so much.
It's really a privilege to be here among such learned and smart people. I will try to supplement what has already been said.
I'm a professor of entrepreneurship and innovation at Toronto Metropolitan University. I was also the vice-president of research and innovation, so I'm very invested in and committed to issues around the commercialization of technology in Canada. At the same time, I'm part of a number of big studies that are focused on responsible use, and I think we've heard a lot about the risks associated with artificial intelligence that have to be taken seriously.
I previously submitted a brief to the AI task force, and I'm happy to provide it to this committee. It reinforced a lot of the points that have already been made about infrastructure development, about sovereignty and the limits of sovereignty, about the urgent need for a regulatory framework for increased risk and to create some measure of certainty, and about the importance of balancing risks and rewards.
What I want to focus on today, though, because I didn't hear anybody talking about them, are the issues around adoption, government as a model user, bias in AI, and skills. I'll try to be brief.
The AI paradox in Canada is that we have a Nobel Prize winner in the development of the technology, yet if you look at us in comparison with other OECD countries, we're laggards in terms of adoption. There are a lot of reasons we can point to to explain that, but one of the most important ones is that we are a country of small and medium-sized enterprises.
We hear a lot about what large corporations are doing. Think about your ridings and who the big employers are. It's not just large companies. Large companies in Canada account for about 10% of private sector employment. What they do is important, but so is what the SMEs do. They provide 90% of the employment, and I think they are often left out of these discussions.
When I talk about SMEs, I'm not just talking about AI start-ups; I'm talking about family businesses in agriculture, in manufacturing, in retail and so on. We really have to grapple with the fact that SMEs in Canada need support in order to grow, address productivity and innovate.
A lot of the focus on AI adoption is around job displacement. That will happen, without question, but that's more likely to happen in large corporations that are using AI to lay people off. Small companies can punch above their weight and can look much bigger than they are if they use AI tools correctly. When we talk about AI tools, we're not just talking about machine learning; we're talking about simple, off-the-shelf services, generative AI and so on. That's one point I would like to emphasize.
Government has a role as a model user. We learned this with the early days of the Internet. Government can do a lot to advance opportunities for start-ups in this space, and I think we see signs that they're moving in that direction.
We have to focus on human capital, and there is a preoccupation with science, technology, engineering and math. They're absolutely critical. We need deep AI skills, and it would be nice to have another Nobel Prize winner, but science, technology, engineering and math are actually what you need to create AI tools.
We need a lot of other skills to advance innovation, and Canada continually makes the mistake of confusing invention with innovation. Innovation is about doing things differently. That means we need lawyers, ethicists and people who understand consumer behaviour, organizational behaviour and markets.
Our biggest barrier to innovation in this country, in my view—I'm biased because I'm in a business school—is the lack of attention on markets and who is going to use the stuff, and for what purposes. While deep AI skills are critical and AI literacy for everyone is important—because all jobs will be affected and all of us need to be protected—the AI skills for innovation, where we take people who understand their businesses and processes and give them the tools to use AI for a responsible purpose, is where I see one of the biggest gaps.
The final thing I'll say, because I am from the Diversity Institute, is that we need to double down on ensuring that AI is not reinforcing bias in the use of biased data and the use of homogeneous teams. We need to ensure that AI is not reinforcing the digital divide we currently see, based on income, geography, indigeneity and gender. We need to be using AI responsibly and inclusively.
I'll stop there. Thank you.