Partnership on AI and the Quality of work (PAIQ)

Reasons for the study

The Canadian labour market is undergoing an artificial intelligence (AI) revolution. AI's capacity to learn, adapt, and create outputs with increasing independence means that it can automate job tasks across a broad range of occupations, with the potential to substantially change the world of work. Despite this, we have limited understanding of how AI will affect working conditions and worker experiences—whether for better or for worse. We also lack information on the worker groups that may be most advantaged or disadvantaged by AI.

This partnership project aims to address pressing questions of how AI will affect the quality of working lives in Canada. Working with a pan-Canadian multidisciplinary team of 39 researchers and 34 partner organizations, we will conduct a series of research studies to better understand what the increased use of AI in the workplace means for wellbeing of Canadian workers. Findings will be integrated into the design of applied resources to guide healthy and equitable AI adoption in the workplace. This project will also build capacity among early career researchers and partners to work at the intersection of AI, work, and health.

Objectives of the study

  • Monitor and synthesize evidence on how AI advancement, adoption, and use impact job quality and worker health, safety and well-being
  • Estimate the extent to which Canadian workers are exposed to AI, and determine how AI exposure relates to job quality and worker health, safety and well-being
  • Directly observe the effect of AI on workers including how AI may impact job task performance, perceptions of work, and well-being
  • Identify and explain how worker and work context characteristics may play a role in advantage or disadvantage from AI

Target audience

This project will be relevant to a broad range of stakeholders. For policy-makers, our research offers a pathway to modernize labour standards, address the risks posed by AI and craft legislation that safeguards workers. For employers, insights can be used to foster conditions at work that promote worker wellbeing and to ensure that workers are central to AI implementation decisions. Workers and unions can leverage our findings to navigate the opportunities and challenges posed by AI and advocate for improved job quality and working conditions. For AI developers, our results will offer guidance on designing safer and more responsible AI technologies that account for their impact on job quality and worker health, safety and wellbeing.

Project status

Ongoing

Research team

  • Arif Jetha, Institute for Work & Health (PI)
  • Aviroop Biswas, Institute for Work & Health
  • Julie Bowring, Institute for Work & Health
  • Meghan Crouch, Institute for Work & Health
  • Kathleen Dobson, Institute for Work & Health
  • Monique Gignac, Institute for Work & Health
  • Emma Irvin, Institute for Work & Health
  • Qing Liao, Institute for Work & Health
  • Peter Smith, Institute for Work & Health
  • Faraz Vahid Shahidi, Institute for Work & Health
  • Victoria Arrandale, University of Toronto
  • Ebrahim Bagheri, Responsible Artificial Intelligence, Toronto Metropolitan University
  • Ananya Banerjee, McGill University
  • Kean Birch, York University
  • Silvia Bonaccio, University of Ottawa
  • Wendy Cukier, Diversity Institute, Toronto Metropolitan University
  • Valerio De Stefano, York University
  • Mathieu Dupuis, Université Laval
  • Marc Frenette, Statistics Canada
  • Avi Goldfarb, University of Toronto
  • Pamela Hopwood, University of Waterloo
  • Naimul Mefraz Khan, Toronto Metropolitan University
  • Vicki Kristman, Lakehead University
  • Peter Loewen, Cornell University
  • Ellen MacEachen, University of Waterloo
  • Muhammad Mamdani, Unity Health Toronto
  • Jenna Myers, University of Toronto
  • Susan Peters, Center for Work, Health and Well-Being, Harvard T.H. Chan School of Public Health
  • Andrew Pinto, St. Michael's Hospital
  • Laura Rosella, AI4PH, University of Toronto
  • Paula Rowland, University Health Network
  • Frank Rudzicz, Vector Institute and Dalhousie University
  • Jay Shaw, University of Toronto
  • Brendan Smith, Public Health Ontario
  • Maxwell Smith, Western University
  • Abiramy Sriharan, York University
  • Jutta Treviranus, Inclusive Research Design Centre, OCAD University
  • Danielle van Jaarsveld, University of British Columbia
  • Gabrielle Voiseux, University of British Columbia
  • Karina Vold, University of Toronto
  • Viet Vu, The Dais at Toronto Metropolitan University
  • Nicole Wu, University of Toronto
  • Daniyal Zuberi, University of Toronto

Collaborators and partners

  • AI for Public Health
  • Alberta Machine Intelligence Institute
  • Blueprint-ADE
  • CIFAR
  • Canadian Manufacturers & Exporters
  • Center for Work, Health and Well-Being, Harvard T.H. Chan School of Public Health
  • CSA Public Policy Centre
  • Diversity Institute, Toronto Metropolitan University
  • Employment and Social Development Canada
  • EPID@Work, Lakehead University
  • Future Skills Centre
  • Inclusive Research Design Centre, OCAD University
  • Labour Market Information Council
  • MaRS Discovery District
  • Mila - Institut Québécois d'intelligence artificielle
  • Occupational Health Clinics for Ontario Workers Inc.
  • Ontario Ministry of Labour, Immigration, Training and Skills Development
  • Public Health Ontario
  • Public Services Health and Safety Association
  • Responsible Development of Artificial Intelligence
  • Schwartz Reisman Institute for Technology and Society at the University of Toronto
  • Statistics Canada
  • The Conference Board of Canada
  • The Dais - Toronto Metropolitan University
  • UNIFOR
  • United Steelworkers
  • Unity Health Toronto
  • University Health Network
  • Upstream Lab
  • Vector Institute
  • Workplace Safety & Prevention Services
  • Workplace Safety & Insurance Board
  • WorkSafeBC

Funded by

Social Sciences & Humanities Research Council of Canada