Procedure and House Affairs Committee on Oct. 22nd, 2026

[The Chair, Liberal, Chris Bittle]

I call this meeting to order.

Welcome to meeting number 43 of the House of Commons Standing Committee on Procedure and House Affairs.

Pursuant to Standing Order 108(3), the committee is meeting to resume its study of artificial intelligence and Canada's democratic institutions.

Today's meeting is taking place in public in a hybrid format, pursuant to the Standing Orders. Members are attending in person in the room and remotely using the Zoom application.

Before we continue, I would ask all in-person participants to consult the guidelines written on the cards on the table. These measures are in place to help prevent audio and feedback incidents and protect the health and safety of all participants, especially our interpreters.

I would like to remind the witnesses that committee members may ask questions in either English or French. If you need interpretation, please take a moment now to prepare your earpiece or select the listening channel on Zoom in advance to take full advantage of the time allotted for questions and answers.

I'd like to make a few comments for the benefit of members.

All comments should be addressed to the chair. If you wish to speak, raise your hand. The clerk and I will do our best to spot you and create a speaking list.

I'd like to welcome our witnesses for the first panel of today's meeting.

Appearing as individuals, we have Anamaria Crisan, assistant professor at the University of Waterloo, and Kevin Deveaux, barrister and solicitor, by video conference. We also have Sarah Lister, director of governance, rule of law and peacebuilding, from the United Nations Development Programme.

We'll turn to Ms. Lister first, since she's in the room, for five minutes, please.

[Dr. Sarah Lister, Director of Governance, Rule of Law and Peacebuilding, United Nations Development Programme]

Members of the committee, thank you for this opportunity. As I start, I would note that the information provided to this committee is offered on a voluntary basis and should not be understood as a waiver—express or implied—of the privileges and immunities of the United Nations and its officials under the 1946 Convention on the Privileges and Immunities of the United Nations.

The United Nations Development Programme is one of the world's largest providers of electoral assistance, supporting electoral processes in around 50 countries each year. It works with one in three parliaments globally. This gives us a distinctive vantage point on how AI is affecting democratic institutions and how those institutions are beginning to respond. It is from this perspective that I'm making these remarks.

Parliaments are central to deciding how the risks and benefits of AI are distributed, which uses are acceptable and in the public interest, and who is accountable.

First, through lawmaking, parliaments can establish enforceable rules. There is no single model that we see. The Republic of Korea has adopted a principles-based framework. Austria has an AI service centre. Lithuania has a regulatory sandbox. Other countries are adapting existing laws. A context-specific regulatory approach can reduce uncertainty while protecting rights and the public interest.

Second, parliaments are using oversight and budget scrutiny mechanisms, and committees such as this one are holding inquiries. Specialized bodies are being established, and budget reviews are testing whether countries have the workforce and safeguards needed for AI.

Third, parliaments can ensure that AI governance is informed by those most affected, including women, minorities and marginalized communities. Citizens' assemblies, deliberative polling, and consultation linked to formal democratic institutions and processes can help determine acceptable uses and build trust.

Fourth, parliaments should strengthen their own readiness and use AI responsibly. AI can improve transcription, translation, research and public access to information, but as in all public institutions, adoption should begin with a defined public problem—not with technology. Institutions need internal policies, sound data governance, risk assessment, staff capability and meaningful human authority over consequential decisions.

The democratic stakes are at their highest in electoral processes. AI can improve voter information, accessibility, translation, planning, data analysis and cybersecurity, but an error can affect the right to vote. An opaque system can impede challenge, and a cybersecurity failure can undermine confidence in a result. AI-generated misinformation, disinformation and hate speech can fuel online and off-line violence, disproportionately affecting women and marginalized groups, and it can damage the quality of public debate.

A recent UNDP survey of 53 electoral management bodies found that 74% were already using AI in at least one function, but more than half had no internal AI policy, and only 15% had dedicated AI-related infrastructure. That gap between adoption and governance is an immediate concern.

Last month, here in Ottawa, UNDP and Elections Canada convened some 200 electoral stakeholders from 60 countries at a global conference on responsible AI and elections. Their exchanges reinforced the need for institutional governance, shared learning and international co-operation.

The practical agenda is clear: Move from ad hoc experimentation to institutional governance; assess risks before deployment; invest in data governance, procurement and oversight; retain human authority to question outputs; and connect parliaments with regulators, audit institutions, civil society, academia and the private sector.

More than 60 national parliaments are already taking concrete action, yet many still lack the technical expertise and institutional structures to legislate and oversee effectively. International co-operation should help close that capacity gap.

The trajectory of AI is not technologically predetermined. Whether it deepens inequality or expands opportunity, and whether it strengthens or weakens democratic governance depends on political and societal choices. Parliaments are uniquely mandated to make those choices visible, accountable and in the public interest.

In that endeavour, UNDP greatly values its partnership with Canada.

Thank you for your attention.

[The Chair, Liberal, Chris Bittle]

Thank you so much.

We'll now turn to Ms. Crisan for five minutes, please.

[Dr. Anamaria Crisan, Assistant Professor, University of Waterloo, As an Individual]

Thank you to the members of the committee for inviting me to take part in this important discussion.

My research focuses on building human-centred technologies for automating work. Put simply, that means designing AI systems around the needs, values and capabilities of people from the outset rather than considering those things only after a system fails or causes harm. I study how people understand, use and are affected by these systems. I develop new methods and interfaces for incorporating human input into how these systems operate.

For more than a decade, I have studied this across very different decision-making contexts, from how organizations understand their customers to deeply personal choices, such as deciding on a medical treatment when faced with a large amount of complex information. Across hundreds of these in-depth conversations, I have come back with three observations.

First, people have very different abilities to use and understand computer systems. This includes not only accessibility concerns but also the ability to understand the information they are being presented with. With AI, the challenge takes a new dimension, because language becomes the primary medium of interaction, making AI systems appear more humanlike. If AI systems are constituent-facing, some people may not understand that they are interfacing with an AI. This can affect what they disclose, how they interpret the response and whether they know when and how to reach a person. The reverse is also increasingly possible. AI systems can simulate constituents, generate large volumes of correspondence or make automated interactions appear to come from individual people. This can make it harder for public institutions to know whose voices they are actually hearing.

These scenarios are examples of the challenges that can arise when AI mediates the dialogue between constituents and the people meant to represent them. The risk is that the systems begin to narrow or distort what each side hears from the other, weakening the connection that democratic representation depends upon.

This leads to an important second point, which is that automation tends to flatten perspectives. Consider using AI to help sift through thousands of constituent emails. It could categorize them by topic or urgency, or it could produce a concise summary. This can be extremely useful, but those summaries are not neutral. An AI decides how to group correspondences, what central themes matter, what gets highlighted or even what's unusual. There is no gold standard reference that we can easily use to judge the correctness of this process. In addition, the AI-generated summaries can change with a simple revision of the prompt wording.

The same is true when AI helps us research, brainstorm or write legislation. The outputs can sound entirely plausible and reasonable while reflecting particular assumptions or perspectives that are difficult to trace. As we rely more heavily on them, there is a risk that we lose the critical engagement that allows us to notice whether certain perspectives are being prioritized.

Third, the levers that people have to influence these systems are often narrower than they appear. A person may be technically in the loop because they can write a prompt, review a summary or prove an output, but AI systems are designed and optimized to reduce the amount of human intervention required. At the same time, understanding why a system produced a particular result remains difficult. We're seeing a substantial burden to verify the factualness of these systems.

These observations have important implications for Canadian democratic institutions. One is the need for Canadian-specific auditing. We should test these systems in the context in which Canadians will actually encounter them. In particular, when AI is used within the service of our democratic institutions, we should audit not only whether the output is factually correct but also whose perspectives are preserved and whose are missing.

These observations also point to a broader challenge for governance. AI systems are difficult to fully interrogate because of their complexity, and our ability to control how they are used is more limited than we think. So far, much of the governance conversation has focused on frontier models and the companies, mostly U.S.-based, that develop and deploy them. That is important, but it addresses only one part of the ecosystem. Increasingly capable open-weight models can be downloaded, modified and redistributed. Some contemporary open-weight models are now substantially more capable than the first ChatGPT model, which already raised early governance concerns.

To be clear, I believe a healthy open-weight ecosystem can provide important public benefits. These models are attractive precisely because they can be run locally, customized for particular needs and used without relying entirely on a small number of commercial providers. However, these properties also make governance more complicated. Once these models are widely distributed, there are far fewer ways for the original developer to control how they are modified or used. Those modifications can also change the accuracy, biases and behaviour of the original system, sometimes subtly. That means governance cannot rely on controlling the technology itself.

We also need to think carefully about how people and institutions rely on it and how resilient they are when it fails. Part of that resilience is making sure that we do not lose sight of the human capacity to notice when something is wrong.

I'll conclude by saying this: Think of hospitals and their paper protocols when digital systems go down. The point is not to avoid technology; it is to make sure that the institution can still function when technology does not. Our public institutions need the same kind of resilience.

Thank you.

[The Chair, Liberal, Chris Bittle]

Thank you so much.

Mr. Deveaux, you have five minutes please.

[Kevin Deveaux, Barrister and Solicitor, As an Individual]

Thank you, Mr. Chairperson.

Thank you for this opportunity to speak before the committee.

By way of background, quickly, before I get into the substance of my submission, I want to let you know that the agenda shows that I am a lawyer, but I was also a member of the Nova Scotia House of Assembly for nine years. I was the official opposition House leader for four of those years. Since then, I've worked with more than 80 parliaments around the world.

I come to you today not as an AI expert but as someone with practical and extensive experience in working with parliaments, so my submission is mainly going to focus on Parliament and, in particular, some of the more practical aspects of AI being introduced into the parliamentary process.

Let me start with what I think are some of the positives that we can probably see from the use of AI as it evolves.

First of all is public input. I think some of my colleagues, other witnesses presenting, have mentioned this. We're likely to see, if we are not already seeing, a proliferation in the number of submissions that can be made. In oral submissions and written submissions, whether it's a bill inquiry or whether it's an oversight inquiry by a committee, we're going to see this increase. We will also probably see, if we are not seeing already, the vetting of those submissions by AI. The U.K. Parliament has already started using AI as a way of vetting submissions when they receive extensive numbers on any particular issue.

Second would be the drafting of private members' bills. I think the process now is one in which there is engagement of the legal drafters, so the content of the bill, the policy decisions perhaps, are made by a member or by a caucus, and then it is sent to a legal drafter to draft from scratch.

We're probably going to start to see the proliferation of bills, private members' bills in particular, because the ability of AI—and I've seen this myself—to draft a reasonably good first draft of a bill in Commonwealth legal style is pretty good. Legal drafters are then going to be required to focus more on editing those drafts. We're probably going to see the proliferation of private members' bills, because the process of developing them is going to be more easily possible.

Bill analysis and budget analysis are things that, at times, have had improvements. Things like the PBO, of course, have been a great value to parliaments, committees and MPs, but I think we're likely to see individual MPs, their staff, caucus staff, committee staff or whoever doing a lot more budget analysis, whether that be comparative analysis or financial analysis. We're likely to see MPs and committees being able to access, in a timely manner, more analysis around the budget and any given bill.

This might have been mentioned by one of the other witnesses already as well. You're very likely to see a change in translation and interpretation services. That should, in the end, enhance the ability of Parliament to provide those services. As I'm sitting here in Halifax, I'm using Zoom with interpretation, but this is only going to proliferate even further. I think there will be much to gain in translation and interpretation.

The last positive one I want to mention is around transparency. I think about myself and others who perhaps try to find a needle in a haystack when we're trying to find something in Hansard, or people who want to go on the Parliament website and look for particular documents. AI as a tool should allow us to access that information in a much easier manner.

I see these as some of the positives.

Now, having said that, I think there are challenges as well. It's almost the flip side to the positives.

You're very likely to see a proliferation in bills, questions, amendments and motions. They're going to be easier to develop. For example, I could foresee, having worked in opposition, that MPs may want to use written questions and may want to, instead of submitting one or two per session, perhaps now submit 100 written questions. There may be tactics related to that, or there may be very precise and specific questions they want to ask, but you're going to see an increase in the volume of those, and that will have an impact on the workload of staff and of Parliament in general.

We're also going to see a proliferation in the number of submissions from citizens and civil society. We already see organizations that are oversight institutions, such as ombudsmen or freedom of information officers, even the courts, very much noting the increase both in the quantity of submissions and in the size of those submissions. What used to be perhaps a two-page complaint letter is now a 20-page letter, because someone used AI to develop it.

We're going to start to see this in Parliament, and maybe we already have—the number of submissions that are being made. Prepare for that.

There's also the accuracy issue. In the Australian Parliament, there was a recent study done that showed that a significant number of submissions using AI assistance had hallucinations or other factually incorrect statements. This is going to change how Parliament and its staff have to go through.... I'm sure Parliamentary staff are now going through every submission—

[The Chair, Liberal, Chris Bittle]

I have to cut you off there, with my apologies. We're well over time. There will be an opportunity to expand on your answers through questions from the members.

We'll turn, first, to Mr. Calkins for the Conservatives for six minutes.

[Blaine Calkins, Conservative, Ponoka—Didsbury, AB]

Thank you, Chair.

My first question is for Ms. Crisan.

If I understood you correctly, the generational gap between the use of IT.... IT is now evolving into artificial intelligence. People aren't sitting down and coding. You don't have to learn how to write a visual basic script in Microsoft Excel anymore in order to create a functioning spreadsheet. You can talk to a computer as though it's a human being now.

I think you said that this is narrowing the gap. It's basically evening out the playing field so that everybody can now use the underlying information technology structures on which AI depends, on an equal footing.

It levels the playing field, does it not?

[Dr. Anamaria Crisan]

Yes and no.

Compared with writing your own visual basic script, yes it does, because you don't necessarily have to do a course and figure it all out. It levels the playing field in this regard. A lot of the stuff I look at is on how people interpret complex statistical information and reports. It can help with those kinds of things.

We're also seeing, on the flip side, that it's really hard to figure out when these systems are starting to fail. They give the illusion of capability because they produce some great responses, but they also start to underperform very quickly. It's hard for people to notice that. It's hard for people to recover from that. I have seen that in some of my own work as well.

[Blaine Calkins, Conservative, Ponoka—Didsbury, AB]

Discernment is a big issue when it comes to following what AI is outputting.

[Dr. Anamaria Crisan]

I missed the first word. Could you please repeat that?

[Blaine Calkins, Conservative, Ponoka—Didsbury, AB]

Discernment is becoming a key feature for society when it comes to using AI. Is that correct?

[Dr. Anamaria Crisan]

That's correct.

You may have heard the term “AI slop”. In research, we're also encountering that. It's so much easier for people to make a software system, or to make up research and submit to research conferences. It often looks plausible, but it's not quite at the level of the standard. It has a number of vulnerabilities or issues in it. It creates an incredible human burden in terms of verifying that, in part because it's not so bad that it's obviously something to dismiss. It requires time to read, review and write.

That can come from a variety of places. A 20-page-long constituent complaint still needs somebody to read it in order to see what the point is. That takes time.

[Blaine Calkins, Conservative, Ponoka—Didsbury, AB]

Somebody could use artificial intelligence to inundate the constituency office of an MP, an MLA, a mayor, a councillor or whatever the case might be. There are all kinds of nefarious actors out there, both domestic and foreign, who can impersonate people and overwhelm the workflow of a constituency office.

Have we seen any of these threats manifest yet? The question would be for all the panellists.

In the most recent Quebec election, we heard about an AI information page that was a default page used by other AI just by happenstance. By accident, it may have influenced the result of the Quebec election.

Have we had any instances in which nefarious actors...? This committee has been seized with that. Some of our own domestic people are trying to interfere or cause havoc in our electoral process by manipulating the rules. Have we seen any of this elsewhere in Canada or around the world?

[Dr. Anamaria Crisan]

I don't know of instances of this occurring in the Canadian parliamentary context.

We know, for example, that AI-generated content is being created and pushed over social media, specifically to influence perspectives or certain political agendas. That has been well documented. I think it is a concern in North America. It existed previous to AI in terms of bot farms coming from adversaries overseas. AI amplifies that process.

With respect to stuff coming in from constituents for MPs, I don't know of any specific examples but would recommend you explore and study that as part of the committee.

I'll let the other panellists chime in here.

[Blaine Calkins, Conservative, Ponoka—Didsbury, AB]

Okay.

Ms. Lister, you had a comment.

[Dr. Sarah Lister]

I would have to check the specific details of it, and I can do that and get back to you, but I believe that there was a situation in the U.S., for example, in which a public consultation on a bill was overwhelmed by submissions.

It would be helpful to distinguish between nefarious and non-nefarious. I mean, it can be used for both purposes. It could be used to cause chaos in a process, but it can also be used for what those using it consider valid democratic ends.

I can check into that specific case in the U.S.

[Blaine Calkins, Conservative, Ponoka—Didsbury, AB]

Would you not agree, Ms. Lister, that if a small minority of people use this technology in a way that makes it seem as though it is the vast majority of the public's general opinion on something, it could create a bias in the mind of a legislator about how important something is to the broader public interest?

[Dr. Sarah Lister]

Yes, I would agree with that. There is also, on top of that, the broader issue of misinformation and disinformation, which is causing challenges on these issues.

[The Chair, Liberal, Chris Bittle]

Thank you so much.

We'll now turn to Ms. Vandenbeld for six minutes, please.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

Thank you very much, Mr. Chair.

I want to thank all of our witnesses. There's such incredible testimony here that raises more questions and possibly more avenues for our committee to pursue.

I have a number of questions, but I'll start with Dr. Lister.

Sarah, UNDP just came out with a report on AI and electoral administration, and you noted a fairly high number: 74% of election bodies around the world are using some form of AI. I noted that it also says that it's not so much the core function of the elections. It's really more about individuals who might be using ChatGPT, or it might be communications. It's very ad hoc.

I wonder if you could comment a bit about how that can be done in a way that is more purposeful and is not just individuals making choices. You noted that AI uptake is actually higher when there are guidelines. Could you perhaps give us a bit of context about how electoral administrations could best do that?

[Dr. Sarah Lister]

Certainly, and as I noted, we surveyed where electoral management bodies were currently using AI and where they hoped to, where there was interest in doing so. The place in which they are currently using it is more on the back end side, perhaps in communications and rewriting some of the data, but there is interest amongst many electoral management bodies in moving up the chain in terms of getting closer to more issues related to the vote.

What is critical in all democratic processes is the issue of public trust. What came out very strongly from this conference that we held with Elections Canada is that the primary driving factor must be maintaining public trust in electoral processes and in democratic processes more broadly. The consensus from across these electoral management bodies was that, to do that, you needed to have a very clear institutional policy, and this required an explicit statement of the uses of AI that were acceptable within an electoral management body, as well as staff training.

I don't wish to speak for Parliament, but I think Australia has taken a very strong stance in relation to use of AI in the electoral management body, and it might be interesting for the committee to look at what they have done.

[Anita Vandenbeld, Liberal, Ottawa West—Nepean, ON]

Thank you. I appreciate that.

My next question is for Mr. Deveaux.

Kevin, I would ask you almost the same thing because I feel that in Parliament most MPs and their staff are doing it themselves. Everybody is doing it differently. There's not really any one guideline on how we use AI, so it is being done fairly ad hoc, and we're in an environment that is particularly unique. It's an adversarial environment.

We are representatives. There's an expectation of accountability back to the public, and a high expectation of transparency. How would you suggest that a committee like ours, which is where in Parliament we would set those kinds of guidelines for our peers, could go about doing that?

[Kevin Deveaux]

Thank you for the question.

At one level, AI is just like anything else. It's like any other tool being used by a parliament. It requires strategy, policies and standard operating procedures, which are the very mundane things by which any institution operates. They need to be in place.

It needs to be clear that AI is a tool and that it needs to be in the hands of humans. I think one of the other witnesses mentioned this. It doesn't need to be the decision-maker. The worry I have, and others may have, is that at some point, we basically hand over decision-making to AI just because of the expediency of it. There always needs to be some way of ensuring that there is a human element in the process. Any strategy or any approach has to ensure that this is the case.

There also needs to be some thought put into exactly what type of systems.... Again, I'm not an AI expert, but having a system that is beholden to an American tech firm or Chinese open-source, to me, would not be in the best interests of Parliament or Canada. The more that, as an autonomous institution, one of the three branches of government, whether it be at the federal or provincial level.... Parliaments in Canada need to ensure that the system they're using, the data it's based on and the information it is holding are secure. That would also need to be part of the process.

As part of that—this may come up in later questions as well—one thing that is particularly important is the human resources side of this. The workload this is going to impose upon staff, whether they be parliamentary staff, constituency assistants or caucus staff, is going to increase significantly. There needs to be some consideration of how we resource that, how we train them and how we give them the capacity to do this effectively, while also understanding that when you get 50,000 submissions on a bill instead of 50 or 100, it is definitely going to make a big difference in the ability of staff to focus on other things and the time that will be consumed by having to deal with this as well.

[The Chair, Liberal, Chris Bittle]

Thank you so much.

Ms. Normandin, you have the floor for six minutes.