Which workers and jobs will be most affected by machine learning?

Machine learning, a form of artificial intelligence, is increasingly being used by Canadian firms to drive innovation and raise productivity. Because machine learning can adapt and generate outputs with increasing independence, this technology can be used to perform physical or cognitive job tasks across a broad range of industries and occupations. However, the use of machine learning may affect different worker groups in different ways.

This study set out to examine the extent to which different jobs in Canada may be exposed to machine learning—that is, the occupation involves tasks that could be performed by this type of technology. It also examined worker and job characteristics that are related to working in occupations with “high” or “low” machine learning exposure.

To estimate machine learning exposure among Canadian jobs, the researchers used an existing U.S.-based measure. The measure scored 1,000 U.S. occupations based on the degree to which job tasks could be performed by machine learning. These jobs were mapped onto Canadian job classifications.

The researchers then used data from eight years of Statistics Canada’s Labour Force Survey (LFS)—from 2013 to 2019, and 2022—to estimate the number of workers in Canada in occupations in the top 10 per cent and bottom 10 percent of machine learning exposure. They also used LFS data to examine whether education, hourly wages, and job skills were related to workers’ likelihood of being in jobs with high or low machine learning exposure and how these relationships differed for men and women.

The study found that every Canadian occupation included at least some job tasks that could be done by machine learning—but no single job could be done completely by machine learning.

Overall, 1.9 million Canadian workers, or 12 per cent of the Canadian workforce, were in jobs characterized by high machine learning exposure. Women were more likely to be employed in these occupations (63.4 per cent) than men (36.6 per cent).

In contrast, about 744,000 workers, or 4.7 per cent of the Canadian workforce, were employed in jobs characterized by low machine learning exposure. Men were more likely to be in these jobs (59.9 per cent) than women (40.1 per cent).

Workers with more education, higher earnings and jobs that demand higher skills were less likely to be exposed to machine learning. The proportions of workers in either high or low machine learning occupations, based on these characteristics, are outlined in tables 1, 2 and 3:

Profile of workers in occupations with low or high machine learning exposure:

How does machine learning exposure vary by gender?

Additional differences—some unexpected—were found between women and men when it came to the relationship of education and job skill requirements to machine learning exposure.

While there was a direct relationship between higher levels of education and greater likelihood of low machine learning exposure among women, this same graded relationship was not present among men. Men with a college or university degree were less likely to be in jobs with low machine learning exposure than men with some post-secondary schooling or less.

The findings of this study suggest that though all workers should be prepared for machine learning in their occupations to some degree, policy and programs to address any impacts should focus on occupations and worker groups that are most likely to be affected by the technology.

Like other technological transformations that have shaped Canada’s labour market, there is potential for vulnerable segments of the workforce to be working in occupations that are most affected by machine learning. It is not clear based on this study whether workers in jobs with high exposure to machine learning will experience advantages or disadvantages stemming from this technology. However, the disproportionate impacts of machine learning on women, as well as on certain workers with lower educational levels, those in lower-paying jobs, and occupations with minimal job skills requirements raise concerns about potential inequities reinforced or widened by machine learning adoption in the labour market.

Nuances in the findings should also be considered when targeting interventions. For example, those with the highest and lowest levels of education had fewer jobs with high exposure to machine learning. In contrast, those in the middle-ground, with trades certifications or a bachelor’s degree, had more jobs with high exposure.

This study is one of the few that provides an overview of potential machine learning exposure in different occupations across Canada, and how this exposure varies according to sociodemographic and occupational characteristics.

The results of this study represent estimates and should not be interpreted as the amount of actual use or adoption of machine learning within these occupations in the coming years. The researchers were unable to speculate on the specific impacts machine learning will have on workers and job performance. Additionally, the suitability for machine learning measurement may not encompass all forms of artificial intelligence and work automation that may continue to be developed as the technology rapidly advances.