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http://dx.doi.org/10.22693/NIAIP.2020.27.4.047

An Exploratory Study on Policy Decision Making with Artificial Intelligence: Applying Problem Structuring Typology on Success and Failure Cases  

Eun, Jong-Hwan (Public Policy Center of Intelligent Society and Policy)
Hwang, Sung-Soo (Department of Public Administration, Yeungnam University)
Publication Information
Informatization Policy / v.27, no.4, 2020 , pp. 47-66 More about this Journal
Abstract
The rapid development of artificial intelligence technologies such as machine learning and deep learning is expanding its impact in the public administrative and public policy sphere. This paper is an exploratory study on policy decision-making in the age of artificial intelligence to design automated configuration and operation through data analysis and algorithm development. The theoretical framework was composed of the types of policy problems according to the degree of problem structuring, and the success and failure cases were classified and analyzed to derive implications. In other words, when the problem structuring is more difficult than others, the greater the possibility of failure or side effects of decision-making using artificial intelligence. Also, concerns about the neutrality of the algorithm were presented. As a policy suggestion, a subcommittee was proposed in which experts in technical and social aspects play a professional role in establishing the AI promotion system in Korea. Although the subcommittee works independently, it suggests that it is necessary to establish governance in which the results of activities can be synthesized and integrated.
Keywords
algorithm governance; artificial intelligence; policy decision making; problem structuring; evidence-based policy; data-based administration;
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Times Cited By KSCI : 4  (Citation Analysis)
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