Machine learning and the labor market: A portrait of occupational and worker inequities in Canada
Open accessAbstract
Introduction Machine learning (ML), an artificial intelligence (AI) subfield, is increasingly used by Canadian workplaces. Concerningly, the impact of ML may be inequitable and contribute to social and health inequities in the working population. The aim of this study is to estimate the number of workers in occupations with high, medium, and low ML exposure and describe differences in exposure according to occupational and worker sociodemographic factors. Methods Canadian occupations were scored according to the extent to which they were made up of job tasks that could be performed by ML. Eight years of data from Canada's Labour Force Survey were pooled and the number of Canadians in occupations with high, medium, or low exposure to ML were estimated. The relationship between hourly wages, educational attainment, and job skill, training and experience requirements, and ML exposure was examined using multinomial models that were stratified by gender. Results Approximately 5.7 million Canadians are working in occupations characterized by high ML exposure. Women workers and workers with a college or bachelor's degree and in occupations with lower job skills requirements made up a greater proportion of workers in occupations with high ML exposure. Multinomial models indicated gender differences in the relationship between independent variables and ML exposure. Among men, higher educational attainment and hourly wages were associated with high occupational ML exposure. However, am…
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A worker's social position (e.g., age, gender) may be associated with employment in occupations with varying levels of AI exposure and precarity.
“In addition, there is evidence indicating that a worker’s social position (eg, age, gender) may be associated with employment in occupations with varying levels of AI exposure and precarity (43).”
From Large language model exposure and precarious occupations: Unpacking relationships in the Canadian labor force (2026) · cites this paper as [43]
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- Authors
- Jetha, A., Liao, Q., Shahidi, F. V., Vu, V., Biswas, A., Smith, B., & Smith, P.
- Affiliations
- University of Toronto (CA)
- Institute for Work & Health (CA)
- Corresponding author
- Jetha, Arif
- Venue
- Social Science & Medicine
- Publisher
- Elsevier BV
- Published
- 2025-09
- Volume
- 381
- Pages
- 118295
- ISSN
- 0277-9536 (ISSN-L)
- OpenAlex
- W4411150360
- Cited by
- 3
- References
- 27
- Citations by year
- 2026: 3
- FWCI
- 2.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Social Science & Medicine (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Government of Canada Social Sciences and Humanities Research Council (890-2021-0018, Future Skills Centre)
- Arthritis Society
- Institute for Work and Health
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Digital Economy and Work Transformation
- Keywords
- artificial intelligence in the workplace, gender differences, women's employment, educational attainment, hourly wages, job skill requirements, labor market
- MeSH terms
- Machine Learning, Adult, Canada, Educational Status, Employment, Female, Humans, Male, Middle Aged, Occupations, Surveys and Questionnaires, Salaries and Fringe Benefits
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