Machine learning might exacerbate workforce inequities: Study

Author Sharlene Gandhi

New research has found that women and those in low-income professions are more likely to be employed in jobs that have a high degree of exposure to machine learning – and, therefore, risk being automated. 

Research conducted by the Institute for Work & Health, the Future Skills Centre, and The Dais divided jobs into two categories based on high and low exposure to machine learning. They found that about 12 per cent of the total workforce were completing tasks that may be suitable for machine learning. 

Part of the federal government’s $2.4 billion AI investment includes $50 million dedicated to providing new skills and training for those whose careers and communities may be disrupted by artificial intelligence. This is through the Sectoral Workforce Solutions Program. 

The federal government specifically mentions the creative industries in this proposed funding. According to the Institute for Work & Health, creative professions could have varying degrees of exposure to machine learning: photographers and patternmakers are likely to encounter more machine learning, while dancers, musicians and advertising professionals may not see their jobs as affected.

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  • Sharlene Gandhi is the Future of Good editorial fellow on digital transformation.

    Sharlene has been reporting on responsible business, environmental sustainability and technology in the UK and Canada since 2018. She has worked with various organizations during this time, including the Stanford Social Innovation Review, the Pentland Centre for Sustainability in Business at Lancaster University, AIGA Eye on Design, Social Enterprise UK and Nature is a Human Right. Sharlene moved to Toronto in early 2023 to join the Future of Good team, where she has been reporting at the intersections of technology, data and social purpose work. Her reporting has spanned several subject areas, including AI policy, cybersecurity, ethical data collection, and technology partnerships between the private, public and third sectors.

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