In this piece from from the archives (originally published January 16, 2020) ICTC’s Faun Rice
interviews colleagues Ryan McLaughlin
and Trevor Quan
on how they approached the question, “what impact will AI have on Canada’s tech ecosystem and labour market?”
The impact of artificial intelligence (AI) on the future of Canada and the world is much debated. In February 2020, the Information and Communications Technology Council (ICTC) released a study on Canada’s AI ecosystem. ICTC Research and Policy Analyst Faun Rice interviewed the study’s authors, Ryan McLaughlin and Trevor Quan, to learn about their research process and findings. In this interview, the team gives a preview of the study with a discussion of the importance of conceptually separating AI from robotics, the relationship between AI and the recession, and upcoming ethical issues in AI research, development, and implementation in Canada.
Faun: Starting off with the basics, what are some of the main research questions that shaped this study?
Trevor: The central goal was to map out Canada’s AI ecosystem, but that’s, of course, a big topic. We examined AI activity by region, the socioeconomic impacts of AI in Canada, areas of opportunity for the use of AI in different strategic sectors, and the ethical challenges in the use of AI. To start off with, we had to frame “what is AI” so that we were talking about the same thing. It can vary by context and industry. So we began by scoping out a definitional framework.
Ryan: Trevor was responsible for a lot of the qualitative work and context, so he looked at the history, what the actual technology is, what’s going on with the ethics, and where AI is headed. My piece focused on labour-force impacts.
Faun: Earlier in this interview, you mentioned that many of the most AI-suitable skills you found are used in jobs that are lower-income, and you started to talk about the policy implications around this. Was there any job that had a high ranking that came way out of the left field and surprised you?
Trevor: Yes, we tried to consider wages in our analysis because there are some jobs that would be easy for an AI to do, but the wages are so low that it isn’t really costing employers much to have humans doing those jobs currently. On the other hand, jobs like financial analysts, some law-related roles, or something like medical imaging, where you have a lot of AI-suitable skills that are very costly. So that’s something we thought about. But it’s also important to think about professional and local contexts-whether more powerful industry associations or regulations will have an impact on speed and style of AI-introduction, or whether you’ll see different effects in regions with different minimum wages.
Faun: You mentioned that interesting bit about people changing jobs rather than facing unemployment. Is there any study that takes a look at career changes as a result of any kind of automation?
Ryan: That’s beyond the scope of this project, but it’s an interesting proposition. Companies like LinkedIn might know that if people are self-reporting accurately. Typically, independent researchers wouldn’t be able to know because all national surveys here are anonymized. You can’t usually track people from category to category. You just get snapshots without identifiers.
Trevor: When I was completing interviews for this project, it’s natural to ask what will happen in the future of different jobs. But for a lot of the people working in AI for a really long time, most of them were reluctant to guess or unsure of how to accurately predict these trends. So impacts will be visible over a long period of time and difficult to isolate.