Abstract
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 co-authorship 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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Source: Authors’ elaboration based on IEEE Xplore and taxonomy. The size of the intervals was automatically chosen by the software
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Data availability
Upon request, we can make available our database, but this cannot be disclosed publicly because of IEEE’s API terms and conditions.
Notes
The search was performed over all publication fields in the database, using “artificial intelligence”. The data was obtained through the IEEE Xplore API using PhP.
\(Accuracy = \frac{TP + TN}{{TP + TN + FP + FN}}\); \(Balanced accuracy = \frac{TP}{{TP + FN}} + \frac{TN}{{TN + FP}} / 2\)
\(Recall = \frac{TP}{{TP + FN}}\)
\(Precision = \frac{TP}{{TP + FP}}\)
\(F_{{\upbeta }} = \left( {1 + {\upbeta }^{2} } \right)\frac{Precision \times Recall}{{\left( {{\upbeta }^{2} \times Precision} \right) + recall}}\)
In the terms of this study, kappa can be described as \(k = \frac{{2 \times \left( {TP \times TN - FN \times FP} \right) }}{{\left( {TP + FP} \right) \times \left( {FP + TN} \right) + \left( {TP + FN} \right) \times \left( {FN + TN} \right) }}\)
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Acknowledgements
This paper substantially extends the proceedings paper presented at the ISSI 2021 conference (Chavarro et al., 2021). We thank the IEEE for offering their open API, OSDGs for their web service to classify papers through machine learning, especially to Gusté Statulevičiūtė and Lukas Pukelis from PPMI. We also thank the Free and Open-source software community: MariaDB, R, Gephi, and Notepad++ teams for making available their software. Special thanks to an anonymous reviewer and to Professor Wolfgang Glänzel for their excellent suggestions.
Funding
This work was supported by the Ministry of Science, Technology, and Innovation of Colombia (MinCiencias) under Grant 848-2019 and the Colombian Society of Engineering Physics (SCIF), Colombia.
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Chavarro, D., Perez-Taborda, J.A. & Ávila, A. Connecting brain and heart: artificial intelligence for sustainable development. Scientometrics 127, 7041–7060 (2022). https://doi.org/10.1007/s11192-022-04299-5
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DOI: https://doi.org/10.1007/s11192-022-04299-5
Keywords
- Artificial intelligence
- Sustainable development
- Engineering and society
- Research policy
- Emerging technologies