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Forecasting extreme labor displacement: A survey of AI practitioners

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  • Gruetzemacher, Ross
  • Paradice, David
  • Lee, Kang Bok

Abstract

While labor-displacing AI has the potential to transform critical aspects of society in the near future, previous work has ignored the possibility of the extreme labor displacement scenarios that could result. To explore this we surveyed attendees of three AI conferences in 2018 about near-to-mid-term AI labor displacement as well as five more extreme labor-displacing AI scenarios. Practitioners indicated that a median of 22% of tasks which humans are currently paid to do could be automated with existing AI; they anticipate this figure rising to 40% in 5 years and 60% in 10 years. Median forecasts indicated a 50% probability of AI systems being capable of automating 90% of human tasks in 25 years and 99% of human tasks in 50 years. Practitioners surveyed at the different conferences had similar forecasts for AI labor displacement this decade, but attendees of the Human-level AI Conference had significantly shorter and more precise forecasts for the more extreme labor-displacing AI scenarios. Interestingly, median forecasts of a 10% probability of 90% and 99% of human tasks being automated were 10 years and 15 years, respectively. We conclude that future of work researchers should more carefully consider these relatively high likelihoods of extreme labor-displacing AI scenarios.

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  • Gruetzemacher, Ross & Paradice, David & Lee, Kang Bok, 2020. "Forecasting extreme labor displacement: A survey of AI practitioners," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
  • Handle: RePEc:eee:tefoso:v:161:y:2020:i:c:s0040162520311495
    DOI: 10.1016/j.techfore.2020.120323
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    3. Piotr Tomasz Makowski & Yuya Kajikawa, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Papers 2103.02395, arXiv.org.
    4. Sławomir Biruk & Piotr Jaśkowski & Magdalena Maciaszczyk, 2022. "Conceptual Framework of a Simulation-Based Manpower Planning Method for Construction Enterprises," Sustainability, MDPI, vol. 14(9), pages 1-13, April.
    5. Egana-delSol, Pablo & Cruz, Gabriel & Micco, Alejandro, 2022. "COVID-19 and automation in a developing economy: Evidence from Chile," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
    6. Jean-Philippe Deranty & Thomas Corbin, 2022. "Artificial Intelligence and work: a critical review of recent research from the social sciences," Papers 2204.00419, arXiv.org.
    7. Gruetzemacher, Ross & Dorner, Florian E. & Bernaola-Alvarez, Niko & Giattino, Charlie & Manheim, David, 2021. "Forecasting AI progress: A research agenda," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    8. Makowski, Piotr Tomasz & Kajikawa, Yuya, 2021. "Automation-driven innovation management? Toward Innovation-Automation-Strategy cycle," Technological Forecasting and Social Change, Elsevier, vol. 168(C).

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