Forecasting AI Risks and Impacts on the Workforce
How AI changes work depends in part on how it is designed, introduced, and governed. FAIRWORK studies what these changes mean for workers’ well-being and livelihoods, and how evidence can inform workplace decisions and public policy.
What we study
Understanding AI and work
AI and automation
- Creative freedom
- Cognitive overload
- Loss of meaning
- Surveillance and control
- Job insecurity
Worker well-being
- Job satisfaction
- Stress
- Health
- Job security
AI and
work
- JobsCreation, displacement, and turnover
- Tasks and skillsTask allocation and job redesign
- Pay and securityWages, benefits, and career prospects
- Working livesAutonomy, satisfaction, and work-life balance
- Working conditionsSafety, workload, and psychological well-being
- Teams and relationshipsCollaboration, supervision, and trust
Differences across workers, industries, and regions
Policy and design responses
- Technology design
- Worker protections
- Education and retraining
- Social safety nets
- Labor and economic policy
Our research
AI and workers
Worker well-being, autonomy, and working conditions
AI and workers
Worker well-being, autonomy, and working conditionsWorker well-being is central to our research on AI and work. We study autonomy, job security, stress, and physical and mental health, alongside changes in tasks and working conditions. We examine changes in jobs, tasks, skills, and wages across workers and industries, alongside regulation, worker protections, organizational decisions, and technology design. Our systematic review examines evidence across disciplines, comparing how researchers define AI, measure its effects, and interpret the findings.
Forecasting the future of work
Comparing estimates, definitions, and assumptions
Forecasting the future of work
Comparing estimates, definitions, and assumptionsA job can be exposed to AI without disappearing. We compare how forecasts define exposure, which tasks and workers they count, and the assumptions that drive different predictions.
How evidence shapes decisions
Workers, managers, and policymakers interpreting research
How evidence shapes decisions
Workers, managers, and policymakers interpreting researchWe use experimental methods to investigate how workers, managers, and policymakers respond to research and forecasts about AI and work. We examine how this evidence influences their beliefs, workplace decisions, and policy preferences.
AI-assisted research methods
Human judgment and reliable research
AI-assisted research methods
Human judgment and reliable researchWe explore how AI can assist research, from reviewing evidence to coding and analysis. We study the reliability and limitations of these approaches, and how to combine them with human expertise.
Current contributors
Students contribute to evidence review, qualitative coding, analysis, and research writing alongside faculty and graduate researchers.
- "Simon" Tran Nguyet Anh An
- Aira Chaudhary
- Anjali Bhati
- Ashley Ko
- Daniel S. Schiff
- Hardik Amin
- Josephine Bradley
- Kayshav Bhardwaj
- Keira Lavelle
- Lucas Wiese
- Luisa Nazareno
- Ronan Romano
- Shiva Shakouri
- Vasundhara Kaul
- Yaalini Senthil Kumar
- Yu Lu