Human Resources Committee on Nov. 20th, 2023

[The Chair, Liberal, Bobby Morrissey]

Thank you, Ms. Hadfield.

Monsieur Lepage-Richer, you have five minutes.

[Théo Lepage-Richer, Social Sciences and Humanities Research Council and Fonds de recherche du Québec Postdoctoral Fellow, University of Toronto, As an Individual]

Good morning, and thank you very much.

My name is Théo Lepage‑Richer, and I'm a post-doctoral researcher at the University of Toronto.

First, I want to thank you for the opportunity to share a few thoughts with you today. These are the product of my research on artificial intelligence governance, a topic I address by combining historical research with public policy analysis.

In previous meetings, several members of the committee raised the following question: how can we develop governance frameworks adapted to technologies that are evolving as quickly as artificial intelligence? This is indeed a legitimate issue that is regularly raised by the providers of this technology to encourage some restraint by public policy-makers. However, I'd like to qualify this question by pointing out the broader trends that the history of artificial intelligence in Canada highlight.

The first federal AI programs provide a useful historical precedent to examine the impact of this technology on the organization of work.

Starting in the 1960s, the Pearson government identified artificial intelligence as a promising technology to reduce the costs associated with hiring qualified public servants.

In 1965, the National Research Council of Canada was mandated to develop a first artificial intelligence program to address the translation of official documents from English to French. As a strategy, program managers opted for the development of software tools that would allow the translation process to be broken down into simple sub-tasks. One of those tools, for example, was designed to produce literal translations of common names and verbs in a text, with the idea that operators would then take care of them by fine tuning them, adding the necessary determiners and revising everything. The purpose of these tools was to standardize specialized tasks such as translation, to the point where they could be assigned to workers without prior training, and, above all, at a lower level on the pay scale.

Although inconclusive, this program launched a series of reforms aimed at reducing the federal government's dependence on skilled workers and, above all, restoring a certain level of control over the federal machinery.

Under Pierre Elliott Trudeau, initiatives such as the CANUNET network and the Télidon system were put in place to create the necessary infrastructure to produce new data on the work of federal employees. In a recent article published by Fenwick McKelvey and myself, we suggest that the objective of these programs is to quantify the work of public servants so that it can be framed more narrowly using new data analysis tools developed in government and elsewhere.

Fifty years later, the applications of artificial intelligence in the Government of Canada and elsewhere have changed. However, there are early warning signs of broader trends that can be identified in these early programs. Rather than completely replacing positions, artificial intelligence tends to be deployed in such a way as to restructure tasks so that they are assigned to workers with more precarious status, limit the opportunities that workers have to exercise their judgment, reduce the dependence of organizations on certain forms of expertise and replace investments in training and workforce development.

These trends go beyond artificial intelligence, of course. However, as Paola Tubaro and her colleagues point out, these trends nevertheless tend to characterize the platforms, management practices, reforms and business models that depend on the deployment of this technology.

As such, it is therefore urgent that the impact of artificial intelligence on the workforce become a key perspective for developing tailored policy responses. This position is shared by a number of people, including Emanuel Moss and Valerio De Stefano, who point out the inability of the risk-based approach that characterizes the current regulatory instruments to account for issues related to worker protection. To reflect the impact of artificial intelligence on the workforce, these instruments would have to take into account the impact of this technology on the distribution of wealth, the quality of jobs and the loss of salaried jobs to precarious or subcontracting positions.

Until now, artificial intelligence has been perceived in Canada as an industrial policy issue, and not without success. However, it is crucial that investments in the AI industry complement, rather than replace, similar investments in human capital.

While future applications of AI are difficult to predict, the structural effects of AI on the organization of work remain stable and can therefore inform policy responses to the test of technological change.

Artificial intelligence is a challenge both in labour law and in industrial policy. I therefore encourage the members of this committee to consider the trends in which artificial intelligence has been embedded over the past 60 years to put in place the necessary safeguards to ensure that workers also benefit from the deployment of this technology.

Thank you very much.

[The Chair, Liberal, Bobby Morrissey]

Thank you, Mr. Lepage‑Richer.

We now have Ms. Jansen for five minutes, please.

[Nicole Janssen, Co-Founder, AltaML Inc.]

Thank you for the invitation to share my thoughts with the committee today. My name is Nicole Janssen. I am the co-founder and co-CEO at AltaML. It is the largest pure-play applied AI company in Canada. We create custom AI software solutions for enterprise-level clients in both the private and public sectors. We're not quite six years old, but we've already worked with over 100 companies on over 400 AI use cases.

I base my thoughts today on my observations of those projects and the current and near-term capabilities of AI, as well as my knowledge of the AI ecosystem.

I want to start by saying that AI will absolutely disrupt the Canadian labour force at all levels and professions and across all sectors, and that the work this committee is doing to understand those impacts is incredibly important.

I will address the elephant in the room around massive job losses due to AI, which seems to be the largest concern we hear around jobs and AI. Of the 100 companies we have worked with, not one has implemented an AI solution and then made resulting job cuts. What we are seeing is that the AI tools are being used to increase productivity and, in most instances, are capable only of augmenting humans, not fully capable of replacing them.

The fear of job losses from AI is consistent with the fear that has come for hundreds of years when a new technology is introduced. It can be traced as far back as the introduction of the mechanical loom. However, every single new technology advance in history has led to more jobs at higher wages.

Overall, there will be net gains in the job market from AI, but certain jobs will absolutely see disruption. In fact, we're already starting to see that disruption. Jobs that require consuming large amounts of information and synthesizing it, such as content creation, or in the legal profession—paralegals, for example—will see significant disruption, as will jobs that require manipulating large amounts of numerical data, like research analysts or financial analysts. AI can identify trends in the market much faster than a human being can. Jobs that provide some form of external, repeatable assistance—like call centres, receptionists, or even executive assistants—will see disruption. Software engineering will be disrupted and already is being disrupted as AI becomes more and more capable of writing high-quality code.

From what I have seen, these jobs won't disappear, but rather the individuals in them will need to adapt how they do their jobs, and their time will be focused on the higher-value work.

You may have noticed that lots of those jobs I just mentioned are white-collar jobs. AI is designed to mimic cognitive function, and it's likely that higher-paying white-collar jobs will have the most exposure to the technology. That said, industrial robots and drones also use AI technology, and that's the one place I see a high likelihood of replacement of jobs, such as in the factory line or for warehouse workers. This is a transition that we have been seeing for many years, though, through automation. Then there's a likelihood of delivery persons over time being replaced by drones.

Jobs in which a human touch or relationships with people skills are required will become more and more important. While these professions likely will use AI to support them, they will not see the same kind of disruption—so professions such as teachers, nurses, doctors, therapists, human resource managers, sales managers and public relations.

Then we'll also have new jobs emerge in AI development, cybersecurity, ethical oversight, change management for AI integration, for data labelling professionals, hardware specialists for AI and prompt engineers. These jobs didn't exist a few years ago, and now we see job ads for them everywhere.

What's clear is that the people of any profession who choose not to adopt and use the new technology will be the ones who lose their jobs to those individuals who do adopt the technology, because they'll be far more productive. Sectors that adopt AI will be more productive and put more people to work faster. If we use AI to approve building permits significantly faster, that puts a lot of people to work a whole lot faster. If we use AI to perfect our preventative maintenance in our plans, that will ensure more uptime and more work for more people.

The productivity growth that AI has the potential to create is a huge advantage for Canada, as we currently face both a long-term labour shortage challenge and incredibly low productivity as a country.

We must embrace AI with careful consideration and proactive measures, investing in education and training programs that equip individuals with the skills needed in an AI-driven economy. We must also implement policies that ensure inclusivity, diversity and ethical use of AI.

I'm here today to share my thoughts, partly because I know how important this topic is, but also because I knew I could rely on ChatGPT to formulate the version one of my comments, which I could then edit and put my own thoughts to, allowing me the efficiency to say “yes” to this request.

Thank you. I welcome your questions.

[The Chair, Liberal, Bobby Morrissey]

Thank you, Ms. Janssen, for a very informative presentation before the committee this morning.

Before we begin this round, committee members, we may get only one six-minute round. It's going to take 24 minutes, so that's going to run us close. If some of your colleagues want to share, that's entirely up to you, because I feel we'll probably get only the one round.

Having said that, we'll begin with Ms. Ferreri for six minutes.

[Michelle Ferreri, Conservative, Peterborough—Kawartha, ON]

Thanks, Mr. Chair, and thank you to our witnesses for being here today to testify in our study on AI and its implications for employment and the labour force.

Ms. Janssen, if I can, I'll start with you. I really enjoyed your testimony. I loved that you used ChatGPT to help you through this. It's an interesting tool. I see it happening as well with our students in education, using it as a tool.

One of the things you talked about is fear of the change, and that is almost a human psychology thing. We've seen this, as you mentioned, throughout time. It's natural evolution; it's inevitable progression to move forward.

In the past, what was the defining factor that pushed it forward? Do you have any sort of historical reference for that? You talked about the mechanical loom. When we look at vehicles and airplanes, we see that so many people resisted that change, but it ultimately increased prosperity. It did not replace jobs.

[Nicole Janssen]

I'll speak maybe to the projects we've done in AI where we have had pushback from fear. There are a lot of them.

The key is that the individual whose job will be impacted, the person whose workflow will change, has to be a part of the process of developing AI from the outset. They have to feel like they're part of the solution, not that this is being done to them.

They also need to see that the goal in implementation of the AI is not to replace them. As soon as someone feels that their job is going to be taken as the outcome, they, the end user, will absolutely not implement this new technology.

[Michelle Ferreri, Conservative, Peterborough—Kawartha, ON]

Thank you.

That's really helpful. I love that you said on record “augment” and not “replace”. I think that's really important to have on record.

If I may, I'll jump to Mr. Autor.

I liked your testimony in regard to health care, as we have this massive doctor shortage, and we have a lot of advancements happening with AI in health care.

I'm curious as to whether you have any input on privacy. One of the biggest concerns a lot of people have is how this is going to impact privacy, with health care being one of those areas of privacy.

What do you think the government should be keeping an eye on in terms of ensuring citizen privacy when using AI?

[Prof. David Autor]

I think it's a very big issue, and depending on the regulatory regime, I don't think privacy is guaranteed in what can and cannot be tracked. Our phones are full-time surveillance devices that not only know all the things we do but report that information to third parties for money. That is then resold. Privacy will be compromised unless regulation prevents it and unless people have ownership of the right to privacy. I think it's a very serious concern.

If I may, Mr. Chair, I'll respond very quickly to something that Ms. Janssen just said about AI and jobs. I do not think we should take it as a historical fact that technology has always improved jobs. The Luddites were absolutely correct that power mills wiped out their employment. Not only that, but wages didn't rise for six decades, and growth was stunted; starvation increased.

I'm not saying that these advances weren't ultimately beneficial, but these technological changes are never uniformly an improvement for all jobs or all people. There are almost always losers—peoples whose expertise is devalued—and when we make these big transitions, we should be prepared to help people adjust to those transitions. This will not be costless—

[Michelle Ferreri, Conservative, Peterborough—Kawartha, ON]

I'm sorry. I'm going to intervene. Thank you.

I think you bring up valid points about learning from history. Ultimately, however, I think prosperity prevails in advancement. That's why these committees and studies are critical.

On that point, I will go back to Ms. Janssen.

You referred to a lot of white-collar jobs having first access to AI. How do you think we prevent that divide between the haves and have-nots, which could happen—and probably will happen—and the creation of a more polarized society? People will have access to technology that will further them economically, socially, etc.

[Nicole Janssen]

I think companies are incentivized to sell to everyone. Selling only to the elite few will not make profits. As we saw in the past with cars, electricity, radio, computers and mobile phones, the makers of these technologies were highly motivated to drive down their prices until everyone on the planet could afford them. Now, I fully recognize that there are places on the planet where there is no accessibility to the Internet or phones, but those prices continue to be driven downward because there is a desire to reach the entire population. If you look at OpenAI's ChatGPT, it's free. There is access for everyone to use it.

Will we start with the white-collar, higher-wage earners likely using AI first? Absolutely, we will, because it costs a lot to develop AI right now. However, as those prices come down—

[The Chair, Liberal, Bobby Morrissey]

Thank you, Ms. Ferreri and Ms. Janssen.

Mr. Van Bynen, go ahead for six minutes.

[Tony Van Bynen, Liberal, Newmarket—Aurora, ON]

Thank you, Mr. Chair.

This has been a very informative discussion. Had I known what the text of Ms. Janssen's introduction would be, I would have used ChatGPT to develop my questions. Unfortunately, I didn't have the time to do so.

There is concern about the benefits being equitable. I think the point was raised initially by Ms. Hadfield.

How do you feel the government could be part of ensuring that the distribution of wealth and benefits is equitable across the economy?

[Prof. Gillian Hadfield]

Part of the point about thinking about how we will adapt our regulatory environment is also thinking about how we adapt all of our funding, tax and benefit systems, as well. I think it's going to be a combination of involving workers, as Ms. Janssen emphasized, in the transformation process.... If we're starting to see much bigger returns on capital, maybe we need to figure out ways workers can be directly compensated for that, as well.

I think the last point.... This is why I think it's very important for Canada to be focused on driving the responsible adoption of these technologies. It's because of the productivity challenges we face. Ultimately, you need productivity to fund all your welfare and support systems—shorter work weeks and so on.

I think there are a lot of ways to spread the benefits, but it will take some deliberate efforts.

[Tony Van Bynen, Liberal, Newmarket—Aurora, ON]

We've heard that the transition is under way. In fact, it's been evolving over the past 10 to 15 years, or for as many as 50 years. The decisions and regulations seem to be built on lagging information, or on what we've experienced or have been seeing.

What kind of information or data should the government be gathering to monitor, so it can start developing leading indicators to guide policy strategy and development?

I'll start with Mr. Autor.

[Prof. David Autor]

It's difficult, because it's moving so fast, as Ms. Janssen and others noted. It would be helpful, I think, to involve the private sector, in order to try to get a sense. Even in the U.S.—which is not the world's leader in information collection, by any stretch of the imagination—we now do large surveys on who's using AI and what they're doing with it. However, we don't have a good sense.

One thing is to understand what task it is being applied to, in what sectors and for what activities. Another is to look at the nature of how jobs are changing, which occupations are growing or shrinking, and what wages are being paid. It's even ideally about understanding, from workers, how their work is changing. Coming at it from both the workers' and firms' perspectives would be potentially complementary.

[Tony Van Bynen, Liberal, Newmarket—Aurora, ON]

Ms. Hadfield, do you want to add to that?

[Prof. Gillian Hadfield]

Yes. Thank you.

It is moving very quickly, and we need to be thinking about agile methods for gaining increased visibility for government. You want to be very careful not to say you'll do another two-year study, because it's moving much faster than that.

I think there's a lack of visibility for government into how these technologies are developing, because for the first time in history, it's almost entirely behind corporate walls.

I do think it's really important to get that ground level. Again, Ms. Janssen's testimony is very helpful in terms of what this looks like on the ground level.

For the CoCounsel example I gave you, I spoke to law firms that were implementing this and asked if they had laid off all their junior lawyers yet. They said that they actually had more work than they knew what to do with, because they could now take somebody's call one afternoon and be ready by the next day to give them good advice and take steps.

There's actually a lot of unmet demand for effort, but you need to be at the ground level to find that out.

I would say that developing agile methods for increasing government visibility into how things are changing on the ground is critical—like SWAT teams.

[Tony Van Bynen, Liberal, Newmarket—Aurora, ON]

Ms. Janssen, I have only about a minute or so left, and I want to get one more question in.

Do you have any brief additional comments?

[Nicole Janssen]

The only thing I'd add is that only about 20% to 30% of AI that's being built is actually being adopted. You would want to focus your attention specifically on the sectors and companies that are adopting AI.

[Tony Van Bynen, Liberal, Newmarket—Aurora, ON]

Thank you very much.

The next question is for Mr. Lepage-Richer.

What are examples of the top countries that are preparing their workforces to deal with the impacts of AI? Are there examples out there that we could look at as best-case scenarios?

[Théo Lepage-Richer]

That's a very good question. I can't think of an international example off the top of my head.

In fact, we have to think about the hidden costs that are often associated with artificial intelligence. When you interact with a platform like ChatGPT, the human work behind it tends to be somewhat erased. However, behind a system like ChatGPT and all the other artificial intelligence systems that are trained using large amounts of data, humans have to work to label that data, format it and organize it, among other things, which is not a well-paid job.

There are a lot of countries, especially emerging countries, that are going to train a whole workforce to do these tasks at a very low cost. The example that comes to mind is not necessarily an example that Canada wants to emulate because, when we talk about low-paying jobs associated with artificial intelligence, we can think about data labelling. This work is essential to all the artificial intelligence systems we use and develop, but it depends on thousands of workers who manually crunch data for pennies. The international examples that come to mind are not necessarily examples that we want to emulate, but that we want to keep in mind to remember the social and human costs associated with the development of these technologies.

[The Chair, Liberal, Bobby Morrissey]

Thank you, Monsieur Lepage-Richer and Mr. Van Bynen.

Go ahead, Ms. Chabot. You have six minutes.

[Louise Chabot, Bloc, Thérèse-De Blainville, QC]

Thank you.

I'd like to thank all the witnesses.

Mr. Lepage‑Richer, according to the OECD, and as the union representative reminded us during the first hour, artificial intelligence objectives must be oriented toward sustainable development and must be human-centred, and we must act responsibly. You talked about fairness and accountability.

Everyone agrees that artificial intelligence will be deployed, as was the case with robotics and automation. Things are going to change, but I want to talk about what happens when we hit our cruising speed. What does it take upstream, both from a regulatory and ethical standpoint, for this to have a positive effect on the workforce, not a negative effect?

[Théo Lepage-Richer]

The approach currently used in Canada to assess and anticipate risks and the impact of this technology is mainly based on self-assessment. The proposed artificial intelligence and data act promotes the idea that we must create a model so that businesses can govern themselves by taking certain parameters into account, while making sure that the effects on their work are as limited as possible.

One of the problems I see with this approach is that AI is deployed in a very wide variety of sectors. Therefore, at some point, these tools need to be tailored to each sector and industry in which AI is deployed. This will allow us to properly represent the reality of workers and users whose quality of life, work and well-being are directly influenced by this technology.

One of the first ideas that comes to mind is that risk assessment tools should be set aside for different industries. In fact, at all levels of government, there are specific frameworks to assess the environmental, financial, social or human impact. However, we do not see the same degree of precision in the evaluation of this technology when it is deployed.

Off the top of my head, I would say that we need a more specific development of analytical tools.