Browse > Article
http://dx.doi.org/10.14400/JDC.2020.18.12.029

A Study on the Work Type of Machine Learning Administrative Service in Metropolitan Government  

Ha, Chung-Yeol (Division of Public Consulting, Hansung University)
Jung, Jin-Teak (Division of Public Administration, Hansung University)
Publication Information
Journal of Digital Convergence / v.18, no.12, 2020 , pp. 29-36 More about this Journal
Abstract
The background of this study is that machine learning administrative services are recently attracting attention as a major policy tool for non-face-to-face administrative services in the post-corona era. This study investigated the types of work expected to be effective when introducing machine learning administrative services for Seoul Metropolitan Government officials who are piloting machine learning administrative services. The research method is a machine that can be introduced by organizational unit by distributing and collecting questionnaires for Seoul administrative organizations that have performed machine learning-based administrative services for one month in July 2020 targeting Seoul public officials using machine learning-based administrative services. By analyzing the learning administration service and application service, the business characteristics of each machine learning administration service type such as supervised learning work type, unsupervised learning work type, and reinforced learning work type were analyzed. As a result of the research analysis, it was found that there were significant differences in the characteristics of administrative tasks by supervised and unsupervised learning areas. In particular, it was found that the reinforcement learning domain contains the most appropriate business characteristics for machine learning administrative services. Implications were drawn. The results of this study can be provided as a reference material to practitioners who want to introduce machine learning administration services, and can be used as basic data for research to researchers who want to study machine learning administration services in the future.
Keywords
Smart Work; Remote Work; Flexible Work Arrangement; untact administrative service;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Christian Szegedy et al., (2015) "Going Deeper with Convolutions," Proc. IEEE Conference Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1-9, http://mng.bz/JzGv.
2 Large Scale Visual Recognition Challenge 2017 (ILSVRC2017) results, http://image-net.org/ challenges/LSVRC/2017/results.
3 Yunpeng Chen et al., "Dual Path Networks," https://arxiv.org/pdf/1707.01629.pdf.
4 Yonghui Wu et al.,(2016) "Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation," submitted 26 Sept. 2016, https://arxiv.org/ abs/1609.08144.
5 Chung-Cheng Chiu et al.,(2017) "State-of-the- Art Speech Recognition with Sequence-to- Sequence Models," submitted 5 Dec. 2017, https://arxiv.org/abs/1712.01769.
6 Volodymyr Mnih et al., (2013) "Playing Atari with Deep Reinforcement Learning," NIPS Deep Learning Workshop 2013, https://arxiv.org/ abs/1312.5602.
7 David Silver et al., "Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm," submitted 5 Dec. 2017, https://arxiv.org/abs/1712.01815.
8 Varun Gulshan et al., "Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs," JAMA, vol. 316, no. 22, 2016, pp. 2402-2410, http://mng.bz/wlDQ.   DOI
9 J. Bosse, M. Burnett, (2015). European Public Sector Award 2015: The public sector as partner for a better society European Institute of Public Administration, Maastricht, the Netherlands.
10 Accenture (2016). Digital government: Your digital citizens are ready, willing… and wait in Retrieved March
11 R. Ayachi, I. (2016). Proactive and reactive e-government services recommendation Universal Access in the Information Society, 15 (4) pp. 681-697
12 European Commission 2016). eGovernment benchmark 2016: A turning point for eGovernment development in Europe? Final insight report Vol. 1.
13 Hendrik Scholta et al (2019). From one-stop shop to no-stop shop: An e-government stage model, Government Information Quarterly 36(1), pp. 11-26.   DOI
14 Initiative D21, & fortiss eGovernment Monitor (2018). Nutzung und Akzeptanz digitaler Verwaltungsangebote-Deutschland, Osterreich und Schweiz im Vergleich Retrieved March 30, 2018.
15 Kaiming He et al., (2016) "Deep Residual Learning for Image Recognition," Proc. IEEE Conference Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770-778, http://mng.bz/PO5P.
16 D. Linders, et al. (2018). Proactive e-governance: Flipping the service delivery model from pull to push in Taiwan, Government Information Quarterly 35(4) S68-S76   DOI
17 Sirendi and Taveter (2016). Bringing service design thinking into the public sector to create proactive and user-friendly public services, Proceedings of the 3rd International Conference on HCI in Business, Government and Organizations (2016), pp. 221-230