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Artificial Intelligence Technology and Social Problem Solving

  • Yeunbae KimEmail author
  • Jaehyuk Cha
Chapter
Part of the Communications in Computer and Information Science book series (CCIS, volume 999)

Abstract

Modern societal issues occur in a broad spectrum with very high levels of complexity and challenges, many of which are becoming increasingly difficult to address without the aid of cutting-edge technology. To alleviate these social problems, the Korean government recently announced the implementation of mega-projects to solve low employment, population aging, low birth rate and social safety net problems by utilizing AI and ICBM (IoT, Cloud Computing, Big Data, Mobile) technologies. In this letter, we will present the views on how AI and ICT technologies can be applied to ease or solve social problems by sharing examples of research results from studies of social anxiety, environmental noise, mobility of the disabled, and problems in social safety. We will also describe how all these technologies, big data, methodologies and knowledge can be combined onto an open social informatics platform.

Keywords

Social problem solving Artificial intelligence Social informatics platform 

Notes

Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT1) (No. 2018R1A5A7059549).

References

  1. 1.
    IBM: Science for Social Good – Applying AI, cloud and deep science toward new societal challenges. http://www.research.ibm.com/science-for-social-good/
  2. 2.
    MSIT of Korea: I-KOREA 4.0 ICT R&D Innovation Strategy (2018)Google Scholar
  3. 3.
    Kim, N., Hong, J., Kim, H., Kim, S.: Analyzing suicide-ideation survey to identify high-risk groups: a data mining approach. In: International Conference on Green and Human Information, China (2017)Google Scholar
  4. 4.
    Kim, H., Lee, Y., Cha, J.: Mobility among people with physical impairment: a study using geo-location tracking data. In: International Conference on Green and Human Information, China (2017)Google Scholar
  5. 5.
    Lee, J., Kim, J., Choi, Y.: SNS data visualization for analyzing spatial-temporal distribution of social anxiety. In: EDB 2016 Proceedings of the Sixth International Conference on Emerging Databases: Technologies, Applications, and Theory, Jeju, Korea, pp. 106–109 (2016)Google Scholar
  6. 6.
    Kim, J., Jung, J., Cha, J., Choi, J., Choi, C., Oh, S.: Application of network analysis into emergency response: focusing on the 2015 outbreak of the middle-eastern respiratory syndrome in Korea. In: Information, vol. 21, No. 2, pp. 441–446. International Information Institute, Tokyo (2018)Google Scholar
  7. 7.
    Koch, F., Cardonha, C., Gentil, J.M., Borger, S.: A platform for citizen sensing in sentient cities. In: Nin, J., Villatoro, D. (eds.) CitiSens 2012. LNCS (LNAI), vol. 7685, pp. 57–66. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-36074-9_6CrossRefGoogle Scholar
  8. 8.
    Land, K.C., Ferriss, A.L.: Chapter 52: The sociology of social indicators. In: 21st Century Sociology (2006). http://www.soc.duke.edu/~cwi/Section_I/I-19TheSociologyofSocialIndicators.pdf
  9. 9.
    Pentland, A.: To signal is human. Am. Sci. 98(3), 204–211 (2010)CrossRefGoogle Scholar
  10. 10.
    Kleinberg, J., Ludwig, J., Mullainathan, S.: A guide to solving social problems with machine learning. In: Harvard Business Review (2016). https://hbr.org/2016/12/a-guide-to-solving-social-problems-with-machine-learning
  11. 11.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems 27 (NIPS 2014). NIPS Foundation (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Intelligent Information Technology Research CenterHanyang UniversitySeoulKorea
  2. 2.Department of Computer ScienceHanyang UniversitySeoulKorea

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