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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:1:p:115-:d:125604
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    References listed on IDEAS

    as
    1. Sangsung Park & Seung-Joo Lee & Sunghae Jun, 2015. "A Network Analysis Model for Selecting Sustainable Technology," Sustainability, MDPI, vol. 7(10), pages 1-16, September.
    2. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    3. Butts, Carter T., 2008. "Social Network Analysis with sna," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 24(i06).
    4. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
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    Cited by:

    1. Sunghae Jun, 2019. "Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    2. Kayoung Kim & Young Ho Byun & Donghyuk Lee & Noeon Park, 2019. "Understanding the Global Status of Particulate Matter with Respect to Research Topics and Research Networks," Sustainability, MDPI, vol. 11(20), pages 1-16, October.
    3. Sunghae Jun, 2018. "Bayesian Count Data Modeling for Finding Technological Sustainability," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    4. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.

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