The case for AI people really trust

This opinion piece by Dr. Mary Wells, the dean of Waterloo Engineering, was first published by The Hill Times.

For the past decade, Canada has rightly celebrated its role in inventing modern artificial intelligence (AI). Canadian researchers helped lay the scientific foundations for today's AI revolution, and those investments continue to pay dividends.

But the next chapter of AI leadership won't be won by the country with the biggest models or the most research breakthroughs. It will belong to the nation that graduates engineers, scientists, business leaders, and policymakers who know how to design, deploy, and govern AI that people can trust. Governments around the world are asking the same question: how do we accelerate AI adoption while protecting citizens from its risks? Too often, the debate presents a false choice between innovation and regulation. This country has the opportunity to reject that false choice by making trust a competitive advantage. For generations, Canadian engineers have earned public confidence by designing bridges that don't collapse, aircraft that fly safely, nuclear reactors that operate reliably, and medical devices that improve lives. Safety, ethics, and public welfare are not afterthoughts. They are foundational engineering principles taught alongside technical excellence.

Artificial intelligence should be no different.

Rather than simply teaching students how to build increasingly powerful AI systems, we should teach them how to build AI that is trustworthy by design. Future technical leaders must understand not only machine learning and software engineering, but also fairness, transparency, accountability, privacy, cybersecurity, and human oversight. They must learn how to evaluate risk before deployment, monitor deployed systems and recognize when AI should not be used at all. These are not "soft skills." They are core engineering competencies that determine whether AI earns public confidence and succeeds outside the laboratory.

Canada's greatest AI challenge is no longer invention. It is helping thousands of organizations adopt AI responsibly. Small and medium-sized businesses, municipalities, hospitals, manufacturers, and non-profit organizations often know AI could improve productivity, but lack the expertise to implement it safely. That expertise gap is one of Canada's greatest economic opportunities.

This is how Canada can turn its education system into an AI adoption strategy. Our colleges and universities educate hundreds of thousands of students each year; if every graduate left university having helped design, deploy or evaluate AI in a real organization safely instead of simply using AI tools, we would create one of the world's most trusted and AI-ready workforces.

At the University of Waterloo, researchers and students are demonstrating what that future could look like. Through a regional AI coalition, industry, governments and post-secondary institutions are embedding students within organizations tackling real AI adoption challenges.

Students work directly with companies to identify AI opportunities, implement responsible solutions, build organizational capability, and help them use it responsibly.

Students gain real-world experience understanding the technical, ethical, and organizational realities of deploying AI. Companies access emerging talent and responsible AI practices are embedded from the start.

Such an approach would also address one of AI adoption's biggest barriers: confidence. Public trust cannot be manufactured through marketing campaigns. It is earned through consistent evidence that systems are safe, transparent, and accountable. Organizations invest confidently when they understand both the opportunities and the risks.

Canada's goal should be to earn justified trust, not blind trust. Overconfidence in AI can be as dangerous as unwarranted fear. Canadians deserve systems that are worthy of trust because they have been designed, tested and governed responsibly.

This country has long excelled at developing global standards. We helped shape international approaches to aviation safety, nuclear regulation and engineering practice. Artificial intelligence presents another opportunity to lead not because we build the biggest systems, but because we can help define how trustworthy systems should be built.

The countries that lead AI over the next decade will not necessarily be those with the fastest algorithms. They will be those with the most trusted ones.

If we, as Canadian post-secondary education institutions, educate future engineers and technical leaders to build trust into AI from the beginning and give them opportunities to

deploy those skills in organizations across the country, we can create something far more valuable than another AI model.

We can build a country where innovation and public trust grow together.

In an era defined by AI, Canada's greatest export may not be another breakthrough model. It may be the people who know how to build AI the world can trust.