Sustainable technology analysis of artificial intelligence using Bayesian and social network models

Juhwan Kim, Sunghae Jun, Dong Sik Jang, Sangsung Park

Research output: Contribution to journalArticle

7 Citations (Scopus)

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.

Original languageEnglish
Article number115
JournalSustainability (Switzerland)
Volume10
Issue number1
DOIs
Publication statusPublished - 2018 Jan 5

Keywords

  • Artificial intelligence
  • Bayesian inference
  • Patent technology analysis
  • Social network analysis
  • Sustainable technology

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

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