44th Parl. 1st Sess.
November 20, 2023
- 12:14:23 p.m.
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Thank you very much. Good afternoon.
My name is Gillian Hadfield. I'm a professor of law and of strategic management at the University of Toronto, where I hold the Schwartz Reisman chair in technology and society and the Canada CIFAR AI chair at the Vector Institute for Artificial Intelligence. I'm appearing in a personal capacity.
Thank you for this opportunity to speak to you on this subject of such critical importance.
I want to highlight four key aspects of the impacts of AI on the labour market.
First, AI is a general-purpose technology that is likely to transform almost all aspects of our economy and our society.
Second, the latest advances in AI can be adopted relatively quickly, but Canadian businesses to date have been slow to adopt AI.
Third, current AI systems are rapidly evolving to perform highly sophisticated tasks, meaning that high-income and high-education occupations may face the greatest exposure to this latest round of automation.
Fourth, the profound impacts of AI across our economy and society demand regulatory shifts to ensure that the full benefits of AI can be realized.
Let me go through each of these in a little more detail.
First, AI is a general-purpose technology. This means it will transform almost all aspects of our economy and society, similar to the impact of the steam engine or information technology. For example, publicly available large language models such as generative pretrained transformers, GPTs, demonstrate the potential for AI to radically reshape the nature of work. These systems are designed to understand and generate human-like text, including computer code, on a massive scale, increasingly to reason and problem-solve, facilitating an almost unlimited range of applications.
Second, the latest advances in AI can be adopted relatively quickly. ChatGPT's swift integration into everyday applications over the last year demonstrates this and suggests that the most recent strides in AI can be implemented relatively quickly, outpacing the adoption rates seen with earlier iterations of this technology. This presents an opportunity for Canadian business and policy-makers to boost productivity and economic growth; however, the committee should take note that Canada has to date been slow to adopt AI. According to a study by Statistics Canada, only 3.7% of companies were using AI at the end of 2021. Studies conducted by IBM and the OECD also suggest that Canada lags behind other economies according to AI adoption metrics.
Third, AI systems are rapidly evolving to perform highly sophisticated and complex tasks. Specifically, AI is being fine-tuned in sector-specific software applications. A notable instance from my own field is CoCounsel, which is a LLM system built on top of GPT-4, functioning as an AI legal assistant for tasks such as legal research, writing and document analysis. CoCounsel has managed to achieve a higher score on the American uniform bar exam than the average test taker—in fact, 90% of test takers. It is also designed to address inherent risks such as AI hallucinations.
Other examples beyond LLM systems include things like AlphaFold, which has solved the protein folding problem, described by a leading computational biologist as the first time an AI system has solved a major scientific problem. These advancements mean that AI can be harnessed more safely and effectively, particularly in sensitive and cognitively complex domains like law, science and health care.
In one study, OpenAI researchers found that GPT exposure was higher at the higher income and education levels. That's something for us to take into account, thinking about how this would look different than in previous innovations.
This brings me to my final and crucial point. The profound impacts that AI will have across our economy and society demand regulatory shifts to ensure that the full benefits of AI can be realized. Our current legal and regulatory frameworks were designed for a pre-AI era and may restrict innovative and productive uses of AI in workplaces. To harness the benefits of AI, we must update these frameworks to address the unique challenges and opportunities that AI presents. Furthermore, given that the nature of AI is rapidly developing technology, effective governance of AI demands that policy-makers move quickly to adopt an AI-enabling regulatory posture that seeks to properly regulate risks, as we do with all other economic activities, while supporting innovation and investment.
In conclusion, we stand at the cusp of a transformative era, and we should be acting to ensure that the benefits of AI are realized equitably and responsibly.
Thank you.
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- 12:37:50 p.m.
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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.
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- 12:40:25 p.m.
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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.
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- 12:49:13 p.m.
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That's a topic near and dear to my heart. It's hard to say this, but I think actually what we are seeing is evidence that the current versions of large language models have a bigger impact on higher education occupations, so that we won't see that sort of pink collar effect that we may have seen in the past. I do think the legal application that I was talking about—it's true—could displace that paralegal level, which is probably female-dominated. I haven't looked at the statistics on that, but it's actually doing legal work all the way through the ranks of the law firm.
I think it is something for us to pay attention to. I suspect this looks different from how it has looked in previous automation waves, however.
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