Understanding Impact of AI on Labor

Thoughful piece with some supporting visuals.

From The Proceedings of the National Academy of Sciences (PNAS).   https://pnas.org

Toward Understanding the Impact of Artificial Intelligence on Labor

Morgan R. Frank,  View ORCID ProfileDavid Autor, James E. Bessen, Erik Brynjolfsson, Manuel Cebrian, David J. Deming, Maryann Feldman, Matthew Groh, José Lobo, Esteban Moro, Dashun Wang,  View ORCID ProfileHyejin Youn, and  View ORCID ProfileIyad Rahwan

PNAS April 2, 2019 116 (14) 6531-6539; first published March 25, 2019 https://doi.org/10.1073/pnas.1900949116
Edited by Jose A. Scheinkman, Columbia University, New York, NY, and approved February 28, 2019 (received for review January 18, 2019)
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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. .... "

Full PDF, 9 Pages

See also:  Brookings: Putting Workers in the Future of Work.