Artificial Intelligence: The Ambiguous Labor Market Impact of Automating Prediction
By Ajay Agrawal, Joshua S. Gans, Avi Goldfarb. Journal of Economic Perspectives, 2019
546 citations recorded by the source corpus.
Recent advances in artificial intelligence are primarily driven by machine learning, a prediction technology. Prediction is useful because it is an input into decision-making. In order to appreciate the impact of artificial intelligence on jobs, it is important to understand the relative roles of prediction and decision tasks. We describe and provide examples of how artificial intelligence will affect labor, emphasizing differences between when the automation of prediction leads to automating decisions versus enhancing decision-making by humans.
Subfields: Information Systems, Management Science and Operations Research, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/5314b05697d58b27a9a16bc2049e9b98999a81f7
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Citations in the corpus
- Influences of the Industry 4.0 Revolution on the Human Capital Development and Consumer Behavior: A Systematic Review (2020, background): “There is an important role for the institutional framework, but also for the image that a company has in the market [100].”
- Experimental evidence on the productivity effects of generative artificial intelligence (2023, background): “If automation technologies (such as computers) augment existing workers, they can simultaneously benefit 20 workers, capital owners, and consumers by raising wages, boosting productivity, and lowering prices (11-13).”
- Artificial intelligence, firm growth, and product innovation (2024, background): “and improving productivity, for example, by replacing human labor for some tasks (Agrawal et al., 2019) or by increasing operational efficiency through better forecasting and more efficient processes (Basu et al.”
- Can ChatGPT Forecast Stock Price Movements? Return Predictability and Large Language Models (2023, background): “(2014), Hoberg and Phillips (2016), Gaulin (2017), Baker, Bloom, and Davis (2016), Manela and Moreira (2017), Hansen, McMahon, and Prat (2018), Ke, Kelly, and Xiu (2019), Ke, Montiel Olea, and Nesbit (2019), Bybee et al. (2019), Gu, Kelly, and Xiu (2020), Cohen, Malloy, and Nguyen (2020), Freyberger”
- Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “Overall, it is not yet clear how these digital technologies, all of which are evolving rapidly, will coevolve with the reorganization of tasks within occupations and across firms (Agrawal et al., 2019; Lane and Saint-Martin, 2021).”