Automation, AI, and Worker Well-being

Luisa Nazareno, Daniel Schiff 

Georgia State University, Andrew Young School of Policy Studies
Georgia Institute of Technology, School of Public Policy

Recent developments in automation and artificial intelligence promise rapid and possibly unprecedented levels of change in the labor market. Recent discourse surrounding these emerging technologies has focused largely on labor displacement, arguing that labor substitution is a problem while labor complementarity can be construed as a positive force. In our work, we look beyond wages and employment rates, arguing that labor complementarity may not be uniformly positive. In particular, increasing levels of automation in work may impact workers’ wellbeing in the present as well as their expectations about the future. Technology can impact worker stress, difficulty of tasks, levels of monitoring, autonomy, and job security, among other impacts. We use the American Time Use Survey Well-Being Module to ascertain whether workers in highly automated jobs experience different levels of well-being (measured through scales of happiness, stress, tiredness, meaningfulness, etc.,) when performing their jobs. To identify the degree of automation in each occupation, we adapt the methodology of Frey and Osborne (2017), who classify occupations by their susceptibility to computerization. We are currently working on improving our research design and conceptual framework to disentangle the multiple hypothesis and impacts relating automation to worker well-being.