"Toward understanding the impact of artificial intelligence on labor"
Abstract
Rapid advances in artificial intelligence (AI) and automation technologies have the potential to significantly disrupt labor markets. While AI and automation can augment the productivity of some workers, they can replace the work done by others and will likely transform almost all occupations at least to some degree. Rising automation is happening in a period of growing economic inequality, raising fears of mass technological unemployment and a renewed call for policy efforts to address the consequences of technological change. In this paper we discuss the barriers that inhibit scientists from measuring the effects of AI and automation on the future of work. These barriers include the lack of high-quality data about the nature of work (e.g., the dynamic requirements of occupations), lack of empirically informed models of key microlevel processes (e.g., skill substitution and human-machine complementarity), and insufficient understanding of how cognitive technologies interact with broader economic dynamics and institutional mechanisms (e.g., urban migration and international trade policy). Overcoming these barriers requires improvements in the longitudinal and spatial resolution of data, as well as refinements to data on workplace skills. These improvements will enable multidisciplinary research to quantitatively monitor and predict the complex evolution of work in tandem with technological progress. Finally, given the fundamental uncertainty in predicting technological change, we recommend developing a decision framework that focuses on resilience to unexpected scenarios in addition to general equilibrium behavior.
Key Passage
The impact of AI and automation will vary greatly across geography, which has implications for the labor force, urban–rural discrepancies, and changes in the income distribution. The study of AI and automation are largely focused on national employment trends and national wealth disparity. However, recent work demonstrates that some places (e.g., cities) are more susceptible to technological change than others. Occupations form a network of dependencies which constrain how easily jobs can be replaced by technology. Therefore, the health of the aggregate labor market may depend on the impact of technology on specific urban and rural labor markets. (p.6536)
Keywords
Automation, Economic Resilience, Employment, Future Of Work, Artificial IntelligenceThemes
Future of Work, Employment, AI and Computerisation, AutomationLinks to Reference
- http://dx.doi.org/10.1073/pnas.1900949116
- https://www.ncbi.nlm.nih.gov/pubmed/30910965
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6452673
- http://www.pnas.org/cgi/pmidlookup?view=long&pmid=30910965
- https://www.pnas.org/content/116/14/6531.short
- https://www.pnas.org/content/pnas/116/14/6531.full.pdf
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