Measuring the Occupational Impact of AI: Tasks, Cognitive Abilities and AI Benchmarks

Songul Tolan (), Annarosa Pesole, Fernando Martinez-Plumed (), Enrique Fernandez-Macias, Jose Hernandez-Orallo () and Emilia Gomez ()
Additional contact information
Songul Tolan: European Commission – JRC, https://joint-research-centre.ec.europa.eu/index_en
Jose Hernandez-Orallo: Universitat Politècnica de València
Emilia Gomez: European Commission - JRC, https://joint-research-centre.ec.europa.eu/index_en

No 2020-02, JRC Working Papers on Labour, Education and Technology from Joint Research Centre

Abstract: In this paper we develop a framework for analysing the impact of AI on occupations. Leaving aside the debates on robotisation, digitalisation and online platforms as well as workplace automation, we focus on the occupational impact of AI that is driven by rapid progress in machine learning. In our framework we map 59 generic tasks from several worker surveys and databases to 14 cognitive abilities (that we extract from the cognitive science literature) and these to a comprehensive list of 328 AI benchmarks used to evaluate progress in AI techniques. The use of these cognitive abilities as an intermediate mapping, instead of mapping task characteristics to AI tasks, allows for an analysis of AI’s occupational impact that goes beyond automation. An application of our framework to occupational databases gives insights into the abilities through which AI is most likely to affect jobs and allows for a ranking of occupation with respect to AI impact. Moreover, we find that some jobs that were traditionally less affected by previous waves of automation may now be subject to relatively higher AI impact.

Keywords: artificial intelligence; occupations; tasks (search for similar items in EconPapers)
Pages: 36 pages
Date: 2020-04
New Economics Papers: this item is included in nep-big, nep-cmp and nep-pay
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:ipt:laedte:202002

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