Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models

Juhwan Kim, Sunghae Jun, Dongsik Jang and Sangsung Park
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Juhwan Kim: Graduate School of Management of Technology, Korea University, Seoul 02841, Korea
Sunghae Jun: Department of Statistics, Cheongju University, Chungbuk 28503, Korea
Dongsik Jang: Department of Industrial Management Engineering, Korea University, Seoul 02841, Korea
Sangsung Park: Graduate School of Management of Technology, Korea University, Seoul 02841, Korea

Sustainability, 2018, vol. 10, issue 1, 1-12

Abstract: Recent developments in artificial intelligence (AI) have led to a significant increase in the use of AI technologies. Many experts are researching and developing AI technologies in their respective fields, often submitting papers and patent applications as a result. In particular, owing to the characteristics of the patent system that is used to protect the exclusive rights to registered technology, patent documents contain detailed information on the developed technology. Therefore, in this study, we propose a statistical method for analyzing patent data on AI technology to improve our understanding of sustainable technology in the field of AI. We collect patent documents that are related to AI technology, and then analyze the patent data to identify sustainable AI technology. In our analysis, we develop a statistical method that combines social network analysis and Bayesian modeling. Based on the results of the proposed method, we provide a technological structure that can be applied to understand the sustainability of AI technology. To show how the proposed method can be applied to a practical problem, we apply the technological structure to a case study in order to analyze sustainable AI technology.

Keywords: artificial intelligence; patent technology analysis; sustainable technology; Bayesian inference; social network analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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