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|a Das, Subhro
|e author
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|a Steffen, Sebastian
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|a Clarke, Wyatt
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|a Reddy, Prabhat
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|a Brynjolfsson, Erik
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|a Fleming, Martin
|e author
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|a Learning Occupational Task-Shares Dynamics for the Future of Work
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| 260 |
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|b ACM,
|c 2021-11-03T18:33:22Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/137300
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|a © 2020 Copyright held by the owner/author(s). The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large dataset of online job postings to explore how exactly occupational task demands have changed over the past decade of AI innovation, especially across high, mid and low wage occupations. Notably, big data and AI have risen significantly among high wage occupations since 2012 and 2016, respectively. We built an ARIMA model to predict future occupational task demands and showcase several relevant examples in Healthcare, Administration, and IT. Such task demands predictions across occupations will play a pivotal role in retraining the workforce of the future.
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|a Article
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|t 10.1145/3375627.3375826
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| 773 |
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|t AIES 2020 - Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society
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