Measuring the AI content of government-funded R&D projects: A proof of concept for the OECD Fundstat initiative

This report presents the results of a proof of concept for a new analytical infrastructure ("Fundstat") for analysing government funding of R&D at the project level, exploiting the wealth of text-based information about funded projects. Reflecting the growth in popularity of artificial...

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Bibliographic Details
Main Author: Yamashita, Izumi (Author)
Other Authors: Murakami, Akiyoshi (Contributor), Cairns, Stephanie (Contributor), Galindo-Rueda, Fernando (Contributor)
Format: Electronic eBook
Language:English
Published: Paris OECD Publishing 2021
Series:OECD Science, Technology and Industry Working Papers no.2021/09
Subjects:
Online Access:DE-862
DE-863
Summary:This report presents the results of a proof of concept for a new analytical infrastructure ("Fundstat") for analysing government funding of R&D at the project level, exploiting the wealth of text-based information about funded projects. Reflecting the growth in popularity of artificial intelligence (AI) and the OECD Council Recommendation on AI's emphasis on R&D investment, the report focuses on analysing government investments into AI-related R&D. Using text mining tools, it documents the creation of a list of key terms used to identify AI-related R&D projects contained in 13 funding databases from eight OECD countries and the EU, provides estimates for the total number and volume of government R&D funding, and characterises their AI funding portfolio. The methods and findings developed in this study also serve as a prototype for a new distributed mechanism capable of measuring and analysing government R&D support across key OECD priority areas and topics.
Physical Description:1 Online-Ressource (105 Seiten)

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