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Strategies for Optimizing Policy Outcomes through Machine Learning: A Case Study on Korean R&D Project Assessment

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  • Lee, Sangkyu

    (Korea Institute for Industrial Economics and Trade)

Abstract

When employed in artificial intelligence (AI) applications, machine learning (ML) allows AI to recognize patterns in data and predict future outcomes based on these patterns, supporting decision-making. This additionally allows ML to be utilized in the formulation of industrial policies (IPs). However, overreliance on AI for all pol-icies presents several challenges. To harness AI effectively, it is essential to ensure logical clarity and measurability that can be digitally transformed into data, along with the availability of a sufficient amount of data to ensure accuracy and reliability. On the other hand, it is more difficult to use AI in IP design when policies must take into normative as well as economic considerations, or when it becomes necessary to define new norms. These are typically cases in which simple pattern recognition fails to grasp the complexity of various issues at play, making the immediate application of AI application impossible. For instance, situations in which numerous stakeholders hold diverse perspectives can make it challenging to establish clear policy objectives. Additionally, any given problem may include some issues that are fundamentally subjective or normative, and thus incapably of being quantified or measured. This also presents challenges to the effective use of AI. This paper explores the ways in which machine learning (ML) techniques in the field of object classification can contribute to formulating industrial policies. Thank you for reading this abstract of a report from the Korea Institute for Industrial Economics and Trade! Visit us on YouTube: https://www.youtube.com/watch?v=Q36v30l5CV0 Visit us on Instagram: https://www.instagram.com/worldkiet/ Visit our website: http://www.kiet.re.kr/en

Suggested Citation

  • Lee, Sangkyu, 2023. "Strategies for Optimizing Policy Outcomes through Machine Learning: A Case Study on Korean R&D Project Assessment," Industrial Economic Review 23-22, Korea Institute for Industrial Economics and Trade.
    • Handle: RePEc:ris:kieter:2023_022
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    More about this item

    Keywords

    artificial intelligence; AI; machine larning (ML); patterns; data; data analysis; pattern recognition; neural networks; industrial policy; policy design; Korea;
    All these keywords.

    JEL classification:

    • E61 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Policy Objectives; Policy Designs and Consistency; Policy Coordination
    • E69 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - Other
    • I28 - Health, Education, and Welfare - - Education - - - Government Policy
    • L52 - Industrial Organization - - Regulation and Industrial Policy - - - Industrial Policy; Sectoral Planning Methods
    • L52 - Industrial Organization - - Regulation and Industrial Policy - - - Industrial Policy; Sectoral Planning Methods
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • L88 - Industrial Organization - - Industry Studies: Services - - - Government Policy

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