Science and Research Committee on Feb. 12th, 2026

[The Chair, Liberal, Salma Zahid]

I call this meeting to order.

Welcome to meeting number 25 of the Standing Committee on Science and Research. The committee is meeting today on the study of artificial intelligence.

Before we start, the committee needs to adopt a new budget for our study on artificial intelligence, since we have two more meetings than we estimated in our previous budget. There is also a budget for our next study on governance and accountability of federal science policy and institutions. The clerk has sent you both draft budgets by email.

Is it the will of the committee to adopt these budgets?

[Some hon. members]

Agreed.

[The Chair, Liberal, Salma Zahid]

Thank you. Those two budgets are adopted.

Before I get to our witnesses, I would like to make a few comments for the benefit of witnesses and members.

Please wait until I recognize you by name before speaking. Those participating by video conference can click on the microphone icon to activate your mic. Those on Zoom can select the appropriate channel for interpretation at the bottom of your screen of floor, English or French. Please mute yourself when you're not speaking. I remind you that all comments should be addressed through the chair.

With that, I would like to welcome the witnesses for our first panel.

We are joined today in person by Mehmet Murat Kristal, a professor at the Schulich School of Business, York University. We are also joined, via video conference, by Dr. Taylor Owen, Beaverbrook chair in media, ethics and communications, and founding director of the Centre for Media, Technology and Democracy. From Ontario Tech University, we are joined, via video conference, by Dr. Steven Murphy, president and vice-chancellor. We are expecting Dr. Peter Lewis, Canada research chair in trustworthy artificial intelligence. He will join soon.

All witnesses will have five minutes for their opening remarks, and then we will go into rounds of questioning by the members.

We will start with Dr. Kristal.

Please go ahead.

[Professor Mehmet Murat Kristal, Professor, Schulich School of Business, York University, As an Individual]

Thank you.

Chair, vice-chairs and members of the committee, thank you for the invitation to appear today.

My remarks focus on one central message: Canada is not falling behind in artificial intelligence, research or talent, but we are at risk of falling behind in AI execution and productivity impact.

Canada remains a global leader in foundational AI research, ethics and graduate-level talent. We produce world-class ideas, models and researchers. The gap is not in invention but in translation, turning research excellence into large-scale deployment, productivity gains and sustained economic impact.

Over the next decade, countries that lead in AI will not be those that publish the most papers. They will be those that embed AI most effectively into the real economy, influencing finance, energy, health, logistics, public services and critical infrastructure.

The single biggest obstacle to AI adoption in Canada is not technology; it is institutional capability. Canada produces excellent engineers and data scientists. What we lack at scale are AI-literate executives, AI-capable boards, and AI-trained regulators and senior public servants. Without this leadership layer, AI remains siloed, risk-averse and under-scaled, even when funding and technical talent are available.

On employment, the core risk is not mass unemployment but uneven transition. AI primarily displaces tasks rather than entire occupations. The real risks are job polarization, widening productivity gaps across firms and sectors, and skill obsolescence, particularly among mid-career workers. The policy response should not be to slow AI adoption. It should be to align AI deployment with workforce transition, re-skilling and executive training so that productivity gains are broadly shared.

With respect to regulation, Canada should focus on regulating AI systems and outcomes, not just models. Effective governance emphasizes explainability, bias monitoring, auditability, data governance and cyber-resilience. This approach allows innovation to continue, while ensuring accountability where real-world risk occurs.

This leads to Canada's strategic opportunity. Canada does not need to out-scale the United States or China in AI platforms. Our opportunity is to outperform in trusted, regulated, large-scale AI deployment. Canada can lead globally in regulated and trustworthy AI, privacy-preserving AI, and AI deployment in finance, infrastructure and public systems. In these domains, institutional trust is not a constraint; it is a competitive advantage.

From a federal policy perspective, three priorities matter the most: first, executive and public sector capability building, including AI literacy at the board, deputy minister and regulator levels; second, shared national data and AI infrastructure to move beyond fragmented pilots toward scalable platforms; and third, clear deployable standards for trustworthy AI so that organizations can operationalize, certify and scale AI responsibly.

In closing, the biggest policy mistake Canada could make is treating AI as only an innovation issue rather than a national infrastructure issue. AI now underpins economic security, payment systems, energy grids, public services and cyber-resilience. Governing it accordingly through capability, coordination and accountability will shape Canada's productivity and competitiveness over the next decade.

Thank you. I look forward to your questions.

[The Chair, Liberal, Salma Zahid]

Thank you.

We will now proceed to Dr. Owen. You will have five minutes for your opening remarks.

Please go ahead. The floor is yours.

[Dr. Taylor Owen, Beaverbrook Chair in Media, Ethics and Communications and Founding Director of the Centre for Media, Technology and Democracy, As an Individual]

Thank you, Chair and members of the committee, for this invitation to appear today. I was recently asked to contribute to the federal AI strategy task force on the theme of trust and safety. My remarks today draw on that submission, which is publicly available.

I understand that this study is focused on AI research, on recent advances, on the needs of research institutions and on the role of the federal government in promoting a responsible AI research ecosystem. I want to address that final point directly because I personally think it's foundational to all the others.

The government has made it clear that it wants Canadians to adopt AI. This is understandable. I increasingly believe that this technology is transformational and has the genuine potential to improve productivity, reshape public services and strengthen our economic competitiveness. To do this, Canada should certainly invest in AI research infrastructure, fund centres of excellence and recruit world-class talent, but investment alone is not a strategy for adoption. Creating the conditions under which those systems can be safely integrated into public life is equally essential. If the systems that emerge from the Canadian AI research and development are deployed without clear standards for safety, transparency and accountability, and if the public does not trust them as a result, then the entire enterprise is undermined. A responsible AI research ecosystem, then, requires a responsible AI governance ecosystem. The two can't be separated.

Right now, that public confidence is simply not there. Only 34% of Canadians are willing to trust AI systems. Nearly 80% are concerned about negative outcomes on their lives, and 78% fear AI will undermine our elections. Seventy per cent are worried about their children's safety, and close to 80% believe AI threatens their jobs. However, 88% want stronger governance. This is not, then, a literacy gap. Canadians are making reasonable judgments. These systems were deployed without meaningful oversight, and they have seen and felt the consequences. This is a governance gap, and without closing it, neither the adoption the government seeks nor public confidence in AI research will materialize.

My submission to the task force argues that closing this governance gap requires action on three fronts. I think of these as the democratic foundations of AI governance—really the bare minimum we need to do.

The first is citizen safety. AI systems are being deployed at an unprecedented pace, often without meaningful risk assessments. We've seen chatbots fail users in mental health crises and children exposed to harmful content through many AI-powered products. Democratic governance requires independent regulatory authority with the power to mandate risk assessments before deployment and ensure heightened protections for children.

The second is information integrity. AI now both curates and creates our information environment. Synthetic media is proliferating. Provenance is obscured, and citizens cannot distinguish meaningfully between authentic material and fabrication. Democratic governance requires mandatory AI labelling and provenance disclosure, at least for now, and data access frameworks that allow research to study these systems.

The third is democratic legitimacy. Citizens must have meaningful agency over AI systems that affect their lives, recourse mechanisms when AI causes harm, data portability so that users are not locked into products, and structured public consultations so that governance is shaped by the people it affects.

These principles can be translated—and I would suggest easily—into concrete policy. Canada does not need sweeping new AI legislation. Most of these measures can be implemented through two existing frameworks, an amended online harms act that brings AI chatbots into scope and an amended consumer privacy protection act with AI-specific transparency requirements. Both bills had cross-partisan support. Both could be revived quickly.

I would also add that addressing this governance gap is itself a research challenge. Too often, AI funding is understood narrowly as investment in computer science and technical infrastructure, but understanding AI's effect on society and developing policy tools to address that require sustained investment in social science, humanities and interdisciplinary research. Canada has strengths in this area, but it remains significantly underfunded. If this committee is considering the needs of Canada's AI research ecosystem, I would urge you to think broadly about what that ecosystem must include.

Governance is not a constraint on a responsible AI research ecosystem. It is a precondition for it.

Thank you. I look forward to questions.

[The Chair, Liberal, Salma Zahid]

Thank you, Dr. Owen.

We will now go to Ontario Tech University. We are joined by Dr. Steven Murphy, president and vice-chancellor; and Dr. Peter Lewis, Canada research chair in trustworthy artificial intelligence.

Dr. Murphy, you will have five minutes for your opening remarks. The floor is yours.

[Dr. Steven Murphy, President and Vice-Chancellor, Ontario Tech University]

Thank you, Madam Chair.

It's good to be back today. I will be splitting my five minutes with Dr. Lewis.

As we've heard this morning, from a policy perspective AI should always be thought of as a balancing act between innovation and governance—issues of privacy, data management and reducing algorithmic bias.

The opportunity for Canada, I believe, lies in developing trustworthy, human-centred AI applications in areas that contribute the most to Canadian GDP. We've tended, to date, to centralize funding around clusters that build out large language models. I don't see this as a realistic niche that Canada can own, given the U.S. hyperscalers. I do see a rich potential in applications that engender trust and build the social licence for AI with the public.

As we diversify our exports, we need to seize the opportunity to develop applications that balance innovation with responsibility in areas that Canada is known for, including, for example, energy, mining and advanced manufacturing, to name an obvious three. In so doing, we can enhance productivity and GDP while building applications that engender trust. This approach also ensures that we don't spread ourselves too thin or forget which industries drive our economy and our exports. It's not just the right thing to do; it's good business, and it fits the Canadian brand internationally. Just think of the global reputation of our banks as they came through the 2008 financial crisis.

It's my privilege to now turn the floor over to our Canada research chair in trustworthy AI, Dr. Peter Lewis.

[Dr. Peter Lewis, Canada Research Chair in Trustworthy Artificial Intelligence, Ontario Tech University]

Thank you, Dr. Murphy.

Thank you, Mr. Chair.

As Dr. Murphy said, I'm with the Canada research chair in trustworthy AI at Ontario Tech University, and director of our recently launched Mindful AI Research Institute, MAIRI.

We championed, through MAIRI, a vision for a thoughtful, intentional and inclusive approach to AI research and innovation, bringing together more than 70 professors with expertise spanning the social and the technical, providing us with the ability to tackle pressing challenges around AI in a deeply interdisciplinary way.

I'd like to express our thanks to the Canada research chair program, the tri-agency research program, the new frontiers in research fund and other federal research programs that have enabled us, for example, to do the research to inform the recently released new federal standard for accessible and inclusive AI—the first of its kind in the world. This standard ensures that the eight million Canadians living with disabilities will have barriers to using AI removed or reduced.

These programs enable us to do the research needed to understand the trustworthiness of the AI systems being promoted and considered for use in courtrooms to analyze forensic evidence, ensuring that any use of AI enhances objectivity and justice and doesn't undermine or threaten them. They're also enabling us to develop a new generation of reflective AI systems with enhanced social intelligence and safety based on new architectures.

I'd like to make three main points.

The first is that, in Canada, I believe we should diversify our efforts. Today, AI is deeply sociotechnical, and research and innovation must reflect this. We need to hear from all sectors of Canadian society, ensure that we include diverse perspectives and lived experiences, and bring multiple disciplines to bear on developing our understanding of the trustworthiness of AI, its impact and how we develop it well.

The days of AI research being mostly about machine learning are over. Canada's national strategy of focusing on three large machine-learning intensive institutes anchored in major urban centres worked well when the challenge was to get machine learning out of the lab and into businesses and people's lives. Now, as we move to the next phase, the challenge is a different, perhaps a more complex one that requires a new, more diversified approach.

My second point is about regulation and values. How should Canada lead globally in AI? What should AI leadership look like? What is the distinguishing feature in the Canadian approach to AI? I would argue that AI leadership is about values. It's about showing how to do it in the right way—a way that respects dignity and inclusivity, and a way that promotes equity, sustainability and truth. It's not about speed.

It is right to acknowledge the pressure to act quickly and to be agile and forward thinking, and we should acknowledge that the world looks to Canada to show how to do this with a values-first approach. Regulation is needed—and it's important—but it's also not enough.

Finally, I would like us to consider what the post-LLM world looks like. There is a question being asked as to whether we should be investing in competing to build the biggest and best LLMs. The LLM boom may be completing its arc, but through history we see that AI development continues with a new architecture, building on what's gone before and opening up new paths and breakthroughs.

How do we position ourselves well for the future? I would argue that, just as Canada did in the early stages of deep learning, without necessarily knowing that it would come to all this, we should be investing in the seeds of what's to come next and placing some bets.

Thank you. I look forward to your questions.

[The Chair, Liberal, Salma Zahid]

Thank you, Dr. Lewis and Dr. Murphy.

We will proceed to our rounds of questions.

Our first round of questioning will be for six minutes each. We will start with MP Baldinelli.

Please go ahead. You have six minutes.

[Tony Baldinelli, Conservative, Niagara Falls—Niagara-on-the-Lake, ON]

Thank you, Madam Chair.

Thank you to the witnesses for being with us this morning. I'm going to begin with Dr. Owen.

Dr. Owen, on October 9, 2025, the Financial Post published an article titled “'Flawed and broken': Critics warn Ottawa's AI task force is too industry focused”, with critics saying that “the government's approach lacks inclusivity and transparency”.

It's hard to imagine that 28 task force members are enough to cover everything and every aspect and angle there is to know about AI during what was considered a national sprint over 30 days to prepare recommendations to a government. Now that you've gone through the process—I've read the report you submitted—do you believe that other expert areas were adequately represented and/or missing from the task force?

[Dr. Taylor Owen]

Yes, that's a good question.

I think there is a wide range of ways to do these kinds of consultative policy processes. Frankly, this probably wouldn't be the way I would have designed one had I been in charge, but I wasn't.

There are two things that need to be considered in these processes. First, what is the breadth of expertise and voices that are included? On this one, I think it's very clear, as that letter and many have pointed out—including many on the panel, frankly—that this certainly had a directional focus to it. There were not a large number of governance experts on it, for example, or privacy experts, or even civil society members representing those who are advocating for governance and privacy measures.

[Tony Baldinelli, Conservative, Niagara Falls—Niagara-on-the-Lake, ON]

I would suggest that even with regard to AI infrastructure, with regard to the power that would be required, not bringing forward—

[Dr. Taylor Owen]

Agreed.

[Tony Baldinelli, Conservative, Niagara Falls—Niagara-on-the-Lake, ON]

—individuals such as the Canadian Nuclear Association, OPG or others to bring forward their concerns was a big miss by the government.

I'm just taking this from your notes. You talked about how responsible AI needs a governance system, and you talked about a “governance gap” that requires action on three fronts: “citizen safety”, “information integrity” and “democratic legitimacy”. In your submission, you talk about how “trust must instead be built in the democratic infrastructure that governs AI.”

Again, when you look at the concerns that were shared with regard to the task force being industry-heavy.... For example, there was a gentleman, Patrick Pichette, from Inovia Capital, who made a submission. He said Ottawa should declare Cohere, in which Inovia is a major investor, Canada's “national LLM champion, and fuel it with large-revenue contracts”, and that the Toronto-based AI firm should receive deals worth over $1 billion each for applications in the public service and defence.

Right there, do you not believe that, as a task force report and recommendation, it's a conflict of interest? Are we not seeking input to develop a strategy as opposed to trying to recommend that one's own company receive contracts from the federal government?

[Dr. Taylor Owen]

I can't speak for other members or for the intent of this task force.

What I can say is that comment in my submission was referring to ongoing consultative deliberative mechanisms. As we head into a world where our lives and our democracy are fundamentally upended by a technology—which I believe is about to happen—it is incumbent on governments to meaningfully deliberate on how those technologies will be used and integrated into our society with those who will be affected, not just those building it.

I don't think that, to date, the government has taken that seriously. I hope that part of a national AI strategy, which we may see soon, includes meaningful and required deliberative consultation as we move through this technological adoption together as a country.

[Tony Baldinelli, Conservative, Niagara Falls—Niagara-on-the-Lake, ON]

Again, in your comments, you said that AI investment alone is not a solution to adoption. In your report, you say, “AI systems are increasingly embedded in Canadians' daily lives, shaping what we see, the work we do, the decisions we make, and the services we access.”

As part of that national sprint, is it almost too late from a regulatory standpoint to catch up and provide for that democratic or governance gap that needs to be filled? Can we still do it in time, or is it too late?

[Dr. Taylor Owen]

It's a good question.

I think this is an ongoing process. I don't think democratic consultation is a single moment. It needs to be embedded in our democratic process, which it is currently not sufficiently in my view. I wish we had started this five years ago, of course. It's not too late, though. These legislative measures—the ones I spelled out specifically in my contribution—could be done quickly and efficiently, and they would have a meaningful impact. Therefore, I would say yes to meaningful consultation beginning as soon as possible. I think the task force was a form of meaningful consultation; it just wasn't sufficient. There are all sorts of ways we need to do this.

At the same time, we need to put in place the baseline legislative measures that will allow us to minimize what I think are the clear, current risks and harms of these technologies. Doing those two things together, to me, is a bare minimum for a national AI strategy that takes the democratic implications of these technologies seriously.

[Tony Baldinelli, Conservative, Niagara Falls—Niagara-on-the-Lake, ON]

Thank you.

[The Chair, Liberal, Salma Zahid]

Thank you.

We will now proceed to MP McKelvie.

Please go ahead. The floor is yours.

[Jennifer McKelvie, Liberal, Ajax, ON]

Thank you, Madam Chair.

My first question is for Dr. Lewis.

Dr. Lewis, your research is very much around trustworthiness in AI and its responsible use. Could you share with us what you think the federal government's top priority should be in shaping a responsible AI research ecosystem in Canada?

[Dr. Peter Lewis]

Thank you very much for the question. I'll make two main points.

First, I would reiterate a point we made. Dr. Owen spoke about the importance of broad-based consultation and meaningful, deliberative engagement, as part of the democratic process, with groups affected in different ways by the AI systems in their midst. Some of them have a lot of control over those and have decisions over how they can use them to, for example, enhance their businesses. Other people are much more in a position of being subject to the decisions of AI systems, and areas such as explainability, transparency, auditability and regulation become increasingly important. One priority, then, ought to be to meaningfully understand these impacts.

I want to be clear that we really do not have a good handle in general, in terms of research, on the impact that AI is having at the moment. There is a lot of ad hoc evidence emerging. In terms of global or general patterns, this is still very much emerging, and we ought to acknowledge that in the way we do this.

A second point is around the framing of how we think about trust in AI systems. If we're not careful, we can very easily slip into a position of trying to get people to trust AI so that adoption is achieved. This is thinking about it the wrong way around. Trust comes where trust is earned and where trust is warranted—at least it should do this, when we're doing things with the right intentions. Any strategy that is attempting to build and calibrate trust in the general population around AI really ought to acknowledge that people trust systems for different reasons. There are a lot of historical and systemic issues that play into those kinds of questions. There are power dynamics at play. It is not a simple question of how we “drive adoption” when we're thinking about trust.

I'm happy to talk more about that, if that's interesting. I think there's some real nuance needed in that area.

[Jennifer McKelvie, Liberal, Ajax, ON]

Thank you.

My next question is for Dr. Murphy.

Budget 2025 announced $1.7 billion to recruit top international researchers. I'm wondering what sorts of barriers or challenges you see to recruiting AI researchers in Canada. Do we need to do more around the recruitment front?

[Dr. Steven Murphy]

Thank you for the question.

I would first commend the government for those efforts. I think they have been very well received in our sector. They offer an immense possibility, especially in this day and age when we see many people who want to be Canadians or see Canadians who want to repatriate from the United States.

In these areas, when we think about bringing in chairs, taking in the depth and breadth of AI research—so that we are talking about systems based around governance needs, trustworthiness needs, the social sciences and engineering and have the full gamut of people contributing to AI solutions—becomes critical. We can't myopically only hire systems engineers, as great as they are.

By no means has that been any restriction. I would hope that, going forward, we continue to place value right across the academy in terms of people who can contribute to a technology that really has the potential to be the fourth industrial revolution and is already amongst us.

[Jennifer McKelvie, Liberal, Ajax, ON]

Thank you.

Dr. Lewis, I had the opportunity to meet with some of your students, who are doing really great projects. I'm wondering if you could speak to where they're going afterwards. How can we retain their IP and their brilliance here in Canada? What are we doing to retain that knowledge around artificial intelligence?