Measuring the AI content of government-funded R&amp;D projects

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

doi:10.1787/7b43b038-en

OECD Science, Technology and Industry Working Papers

2021

DOI: 10.1787/7b43b038-en

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Measuring the AI content of government-funded R&D projects

Izumi Yamashita¹,

Akiyoshi Murakami²,

Stephanie Cairns³

et al.

Abstract: Note to Delegations:This document is also available on O.N.E under the reference code: DSTI/STP/NESTI(2019)1/FINAL This document, as well as any data and any map included herein, are without prejudice to the status of or sovereignty over any territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.

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Cited by 4 publications

References 15 publications

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Connecting brain and heart: artificial intelligence for sustainable development

Chavarro¹,

Taborda

Ávila

2022

Scientometrics

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A key objective of global policies on Artificial Intelligence (AI) is to foster AI research for sustainable development (SD). In this paper, we analyze the inclusion of SD in AI research indexed by the IEEE Xplore database from 2000 to 2019. We address three critical questions: 1) To what extent is AI research addressing the sustainable development goals (SDGs)? 2) Which subject areas of AI show an emerging interest in SD? And 3) What patterns of collaboration between regions of the world are being stimulated by AI? Our scientometric analysis consists of 1) Identifying the number of AI papers that address SDGs in their titles, abstracts, and keywords. 2) Developing a composite indicator based on the number of documents produced, scientific impact, and inventive impact to distinguish areas with an emerging interest in SD; 3) Exploring coauthorship networks at three levels: region, income group, and country. The overall results show that a small share of papers is explicitly focused on SD. Our composite indicator allowed us to identify an emerging interest in SD from Ultrasonics, Ferroelectrics, and Frequency Control, Education, Consumer Electronics, Electrical Engineering, Electromagnetic Compatibility and Interference. Specifically, on AI subjects, we found emerging interests in Prediction Methods, Computation Theory, Machine Learning, Learning (artificial intelligence), and Biological Neural Networks. Inter-regional and inter-income group collaboration are limited, and network power is concentrated in a few countries. The results could be useful to improve the connection between technical knowledge, strategic planning for S&T investment, and SD policies.

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Connecting brain and heart: artificial intelligence for sustainable development

Chavarro¹,

Taborda

Ávila

2022

Scientometrics

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Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis

Jin,

Chen,

Zhang

et al. 2024

IEEE Trans. Eng. Manage.

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AI for AI: Using AI methods for classifying AI science documents

Sachini¹,

Sioumalas-Christodoulou

Christopoulos

et al. 2022

Quantitative Science Studies

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Subject area classification is an important first phase in the entire process involved in bibliometrics. In this paper, we explore the possibility of using automated algorithms for classifying scientific papers related to Artificial Intelligence at the document level. The current process is semi-manual and journal-based, a realisation that, we argue, opens up the potential for inaccuracies. To counter this, our proposed automated approach makes use of neural networks, specifically BERT. The classification accuracy of our model reaches 96.5%. In addition, the model was used for further classifying documents from 26 different subject areas from the Scopus database. Findings indicate that a significant subset of existing Computer Science, Decision Science and Mathematics publications could potentially be classified as AI-related. The same holds in particular cases in other science fields such as Medicine and Psychology or Arts and Humanities. The above indicate that in subject area classification processes, there is room for automatic approaches to be utilised in a complementary manner with traditional manual procedures. Peer Review https://publons.com/publon/10.1162/qss_a_00223

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Measuring the AI content of government-funded R&D projects

Cited by 4 publications

References 15 publications

Connecting brain and heart: artificial intelligence for sustainable development

Connecting brain and heart: artificial intelligence for sustainable development

Unraveling Scientific Evolutionary Paths: An Embedding-Based Topic Analysis

AI for AI: Using AI methods for classifying AI science documents

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