Identification of future signal based on the quantitative and qualitative text mining: a case study on ethical issues in artificial intelligence

Young-Joo Lee () and Ji-Young Park ()
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Young-Joo Lee: National Information Society Agency (NIA)
Ji-Young Park: National Information Society Agency (NIA)

Quality & Quantity: International Journal of Methodology, 2018, vol. 52, issue 2, No 10, 653-667

Abstract: Abstract To foresee the advent of new technologies and their socio-economic impact is a necessity for academia, governments and private enterprises as well. In the future studies, the identification of future signal is one of the renowned techniques for analysis of trends, emerging issue, and gaining future insights. In the Big Data era, recent scholars have proposed using a text mining procedure focusing upon web data such as new social media and academic papers. However, the detection of future signals is still under a developing area of research, and there is much to improve existing methodology as well as developing theoretical foundations. The present study reviews previous literature on identifying emerging issue based on the weak signal detection approach. Then the authors proposed a revised framework that incorporate quantitative and qualitative text mining for assessing the strength of future signals. The authors applied the framework to the case study on the ethical issues of artificial intelligence (hereafter AI). From EBSCO host database, the authors collected text data covering the ethical issues in AI and conducted text mining analysis. Results reveal that emerging ethical issues can be classified as strong signal, weak signal, well-known but not so strong signal, and latent signal. The revised methodology will be able to provide insights for government and business stakeholders by identifying the future signals and their meanings in various fields.

Keywords: Future signal; Artificial intelligence; Ethical issue; Text mining; Data-driven foresight (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (6)

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DOI: 10.1007/s11135-017-0582-8

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