A Method to Link Advances in Artificial Intelligence to Occupational Abilities

By Edward W. Felten, Manav Raj, Robert C. Seamans. 2018

200 citations recorded by the source corpus.

Subfields: Demography, General Health Professions, Sociology and Political Science, Economics & Operations Research, Social Sciences & Humanities

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Citations in the corpus

GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models (2023, methodology): “Low correlations with (Felten et al., 2018) and (Frey and Osborne, 2017) could potentially be explained by diﬀerences in approaches.”
Occupational, industry, and geographic exposure to artificial intelligence: A novel dataset and its potential uses (2021, methodology): “Our analytical strategy borrows in part from Felten et al.'s (2018) methodology, which links progress in AI applications to construct a backward-looking measure that evaluates the effect of AI on occupations from 2010 to 2015.”
AI and Jobs: Evidence from Online Vacancies (2020, methodology): “Webb’s analysis identifies occupations that are exposed to AI based on whether the occupation’s tasks, measured using O*NET as in Felten et al., overlap with capabilities described in AI patents. Occupations that have a larger fraction of such overlapping tasks are classified as more exposed. The th”
Artificial intelligence, firm growth, and product innovation (2024, background): “See Felten et al.(2018; 2019), Webb (2020), Grennan and Michaely (2020), and Acemoglu et al. (2021), among others.”
New Frontiers: The Origins and Content of New Work, 1940–2018 (2022, background): “On the potential impact of artificial intelligence on jobs, see Brynjolfsson et al. (2018), Felten et al. (2019, 2018), Acemoglu et al. (2020a), Alekseeva et al. (2020), Babina et al. (2020), Grennan and Michaely (2020), and Webb (2020).”
AI and the Economy (2018, methodology): “Felten et al. (2018) provide a method that could be used by other researchers and policymakers to identify which occupations will be most affected by advances in differ‑ ent aspects of AI.”
How will Language Modelers like ChatGPT Affect Occupations and Industries? (2023, methodology): “In order to better understand how language modelers such as ChatGPT will affect occupations, industries and geographies, we use a methodology developed by Felten et al (2018, 2021).”
The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty (2022, methodology): “Drawing on AI-suitability indexes developed by Brynjolfsson et al. (2018), Felten et al. (2018), and Webb (2020), the paper first predicts which establishments are likely to adopt AI as a function of the suitability of their job task structures (visible in job postings) in the pre-AI era.”
AI Adoption and System-Wide Change (2021, background): “…for example, there have been numerous exercises designed to identify jobs at risk from AI, the tasks that comprise jobs that are at risk, and the more general impact of automation on the workplace (Webb (2020); Brynjolfsson and Mitchell (2017); Brynjolfsson et al. (2018); Felten et al. (2018)).”
Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks (2020, background): “Our approach relates most to Felten et al. (2018) who also link AI ﬁeld benchmarks of to work-related abilities but there are some noteworthy diﬀerences.”