Forecasting extreme labor displacement: A survey of AI practitioners

Ross Gruetzemacher, David Paradice and Kang Bok Lee

Technological Forecasting and Social Change, 2020, vol. 161, issue C

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.

Keywords: Artificial intelligence; AI; AI labor displacement (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162520311495
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:161:y:2020:i:c:s0040162520311495

DOI: 10.1016/j.techfore.2020.120323

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().