Toward understanding the impact of artificial intelligence on labor

By Morgan R. Frank, David H. Autor, James Bessen, Erik Brynjolfsson, Manuel Cebrián, David J. Deming, Maryann P. Feldman, Matthew Groh, José Lobo, E. Moro and 3 more. Proceedings of the National Academy of Sciences of the United States of America, 2019

615 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics

View Paper: https://www.semanticscholar.org/paper/16a7b6234e070025a3b6a1e07bd9794d4cdbc2f2

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Citations in the corpus

Generative AI enhances individual creativity but reduces the collective diversity of novel content (2023, neutral): “Much has been written about the potential replacement of human labor by AI (e.g., automation) ( 35 – 37 ) or a “horse race” between human and AI-generated ideas ( 38 – 40 ).”
What is the price of a skill? The value of complementarity (2022, background): “Skill requirements of occupations are dynamic, because technological innovations change the demand for specific skills and thereby the skill composition of occupations – aggregating skills into occupations therefore obfuscates the differential impact of new technologies (Frank et al., 2019).”
The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientation (2023, positive): “AI-enabled systems can have a consequential impact on human lives, especially when used as decision aids in high-stakes contexts, e.g., medicine (2, 3), jurisdiction (4), immigration (5), or hiring (6).”
A review and agenda for examining how technology-driven changes at work will impact workplace mental health and employee well-being (2020, background): “Rapid advances in artificial intelligence, also referred to as machine intelligence, have expanded what is now possible with automation and robotics (Frank et al., 2019).”
Exposure of occupations to technologies of the fourth industrial revolution (2021, background): “Additionally, Frank et al. (2019a) explain in their widely used framework of Industry 4.0, that several base technologies (e.g., cloud computing) support the implementation of “smart” (or intelligent) front end technologies.”
Art and the science of generative AI (2023, background): “Recent research [82,83] demonstrates the need to quantify the specific activities of various artistic workers before comparing them to the actual capabilities of technology.”
AI Adoption and System-Wide Change (2021, background): “To date, our conception of AI adoption has mainly operated at the level of a task or decision (e.g., Frank et al. (2019); Acemoglu and Restrepo (2018)).”
Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “Our understanding of the evolution and impact of these digital technologies should improve over time as we acquire more data (Frank et al., 2019).”