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

Izumi Yamashita, Akiyoshi Murakami, Stephanie Cairns and Fernando Galindo-Rueda

No 2021/09, OECD Science, Technology and Industry Working Papers from OECD Publishing

Abstract: 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.

Keywords: artificial intelligence; government funding; research and development (search for similar items in EconPapers)
Date: 2021-06-28
New Economics Papers: this item is included in nep-big and nep-ppm
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Persistent link: https://EconPapers.repec.org/RePEc:oec:stiaaa:2021/09-en

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