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  2. Partnership on AI and Quality of work (PAIQ)

Analytical cluster

Purpose

The purpose of the PAIQ analytical cluster is to develop analytical approaches to and generate evidence on the diffusion of artificial intelligence (AI) in work contexts, including occupational-specific exposure to AI, and its relationship to job quality, health and wellbeing among workers in Canada.

Expected outcomes

This cluster will use population-level data sets such as Statistics Canada's Labour Force Survey (LFS), a cross-sectional monthly survey of a representative sample of the Canadian working-age population, to produce estimates of the impact of AI as a part of work. This research will build on previous work our team has done on occupational-specific exposure to AI, that is, the extent to which an occupation's tasks can be performed by or with AI systems. We will also explore other uses of AI in work contexts, such as scheduling and/or monitoring of work.

This website will present report descriptive findings as they become available.

Key activities

  • Estimate AI exposure across the workforce: Produce annual estimates of the number and proportion of workers exposed to occupational AI using national labour force data.
  • Analyze job quality impacts: Examine relationships between exposure to AI (such as occupational exposure) and a broad set of employment and working conditions (e.g., hours, earnings, and occupational hazards).
  • Identify labour market inequities: Explore how AI exposure and job quality are patterned by worker and workplace characteristics with the aim of identifying the groups most affected by technological change.
  • Integrate measures of worker wellbeing: Combine national labour force data with additional partner datasets to incorporate indicators of workers’ health and wellbeing.
  • Develop public data tools: Build an interactive data visualization platform and make analytical code and metadata publicly available to enhance transparency, collaboration, and knowledge translation.
  • Enable ongoing collaboration and review: Provide opportunities for project members and trainees to test hypotheses emerging from other research streams and review analytical plans quarterly to refine methods and ensure quality. 

Research team

Chairs and research leads

  • Brendan Smith, Public Health Ontario
  • Faraz Vahid Shahidi, Institute for Work & Health
  • Viet Vu, The Dais at Toronto Metropolitan University

Confirmed members

  • Ebrahim Bagheri, University of Toronto
  • Aakash Bajaj, Lakehead University
  • Aviroop Biswas, Institute for Work & Health
  • Diogo Borba, Signal49 Research
  • Carrie Briley, Workplace Safety and Insurance Board
  • Ken Chatoor, Labour Market Information Council
  • Kathleen Dobson, Institute for Work & Health
  • Rosa Ellithorpe, Alberta Machine Intelligence Institute
  • Mohamed Elmi, Diversity Institute at Toronto Metropolitan University
  • Marc Frenette, Statistics Canada
  • Avi Goldfarb, University of Toronto
  • Vicki Kristman, EPID@Work at Lakehead University
  • Cindy Qi Li, Inclusive Research Design Centre at OCAD University
  • Vivian Vi Li, Labour Market Information Council
  • Peter Loewen, Cornell University
  • Tahsin Mehdi, Statistics Canada
  • Bruno Rainville, Employment and Social Development Canada
  • Cherise Regier, Institute for Work & Health
  • Ryan Rodrigues, Unifor National
  • Laura Rosella, Artificial Intelligence for Public Health  
  • Frank Rudzicz, Dalhousie University
  • Alexander Stephens, Future Skills Centre
  • Rebecca Webb, Labour Market Information Council
  • Tricia Williams, Future Skills Centre

Research analyst

Qing Liao, Institute for Work & Health

Coordinator

Julie Bowring, Institute for Work & Health