Machine learning and the labor market: A portrait of occupational and worker inequities in Canada.

Journal: Social science & medicine (1982)
Published Date: Jun 9, 2025

Abstract

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.

Authors

  • Arif Jetha
    Institute for Work and Health, Toronto, Ontario, Canada.
  • Qing Liao
    Department of Pathology, Nanfang Hospital and Basic Medical College, Southern Medical University, Guangzhou, Guangdong Province, PR China.
  • Faraz Vahid Shahidi
    Institute for Work & Health, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, ON, Canada. Electronic address: fshahidi@iwh.on.ca.
  • Viet Vu
    Department of Cardiac Surgery, Vinmec International Hospital, Ho Chi Minh City, Vietnam.
  • Aviroop Biswas
    Institute for Work & Health, Toronto, Ontario, Canada.
  • Brendan Smith
    Dalla Lana School of Public Health, University of Toronto, ON, Canada; Public Health Ontario, Toronto, ON, Canada. Electronic address: brendant.smith@utoronto.ca.
  • Peter Smith
    Institute for Work & Health, Toronto, ON, Canada; Dalla Lana School of Public Health, University of Toronto, ON, Canada. Electronic address: PSmith@iwh.on.ca.

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