Machine learning and the labor market: A portrait of occupational and worker inequities in Canada
ML exposure is segmented according to occupational and worker sociodemographic characteristics and has the potential to widen inequities in the working population. ML may have a gendered effect and disproportionately impact certain groups of women when compared to men. We provide a critical evidence base to inform strategic responses that ensure inclusion in a working world where ML is commonplace.
Authors
- Faraz Vahid Shahidi (ORCID: https://orcid.org/0000-0002-2789-1515)
- Peter Smith (ORCID: https://orcid.org/0000-0001-8286-4563)
- Qing Liao (ORCID: https://orcid.org/0000-0002-9169-4196)
- Arif Jetha (ORCID: https://orcid.org/0000-0003-0322-7027)
- Brendan T. Smith (ORCID: https://orcid.org/0000-0003-2785-1246)
- Aviroop Biswas (ORCID: https://orcid.org/0000-0002-0393-6280)
- Viet Vu
Institutions
- University of Toronto (CA)
- Public Health Ontario (CA)
- Institute for Work & Health (CA)
Publication Details
- Journal
- Social Science & Medicine
- Published
- 2025-06-09
- DOI
- https://doi.org/10.1016/j.socscimed.2025.118295
- Primary Topic
- Retirement, Disability, and Employment
- Type
- article
- Field-Weighted Citation Impact
- 0.00