KDI Policy Forum Can Artificial Intelligence Improve the Effectiveness of Government Support Policies? July 20, 2022
Series No. No. 288 (2022-03), eng.
KDI Policy Forum
Can Artificial Intelligence Improve the Effectiveness of Government Support Policies?
#Medium-sized Enterprises
#General(Other)
#Central and Local Governments
July 20, 2022
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Minho Kim
프로필
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KDIHAN, Jaepil
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AI technology with excellent predictability can be used in various ways even in the public sector. However, it is said that Korea is still at the level of simply replacing repetitive tasks. If we introduce AI technology, how much can we increase the effectiveness of policies?
Related report:
Can Artificial Intelligence Improve the Effectiveness of Government Support Policies?
https://www.kdi.re.kr/research/forumView?pub_no=17622
Author: Minho Kim, Fellow at KDI
#AI #Artificial Intelligence #Support Policy #Machine Learning #SMEs
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Artificial intelligence (AI) has strong prediction skills that can lead to innovation in numerous public sectors.
Korea has seen AI adoption in the public sector, but it's mostly been to replace simple, repetitive tasks.
Can AI technology predict policy impact for better outcomes and effectiveness?
To answer this question, we applied a machine learning method to Korea's SME support projects, specifically to startups with 0-6 years of operation.
We then compared the project outcomes between two approaches: one with and one without applying a machine learning model.
Let's examine the growth of recipient firms selected in the current process.
One year after receiving government funds, their sales growth was lower than that of non-recipients.
Next, we divided the samples into two groups with high and low sales growth expectations using a machine learning model, and compared these AI-produced growth estimates to the actual performance of those samples.
The results show that the actual sales growth of the top 30% is over ten times higher than that of the bottom 70%, confirming that the machine learning model is highly effective in predicting sales growth.
We then checked recipient firms that fell in the AI-estimated top 30% group and found their actual performance showed strong improvements.
The adoption of AI-applied recipient selection can effectively identify creditworthy firms with growth potential but are financially constrained, improving policy effectiveness.
The public sector's adoption of AI has faced challenges like incomplete standardization and insufficient sharing of policy information between ministries.
Nevertheless, concerted efforts must be made to adopt this technology in high-budget fields like education, health care, and business support that require more effective policy implementation.
What can be done to facilitate the use of AI in the public sector?
We need a national strategy for building a system of data-driven policymaking. The current 'National Strategy for Artificial Intelligence' has been limited to the adoption of AI technology itself, but it's essential to transition public organizations' operations into data-driven policymaking through workplace innovation. Each organization must establish a company-wide strategy that selects necessary data, inspects overall policy establishment, execution, and evaluation, analyzes outcomes, and reflects them in the policy process.
To improve policy effectiveness, we need detailed strategies for the use of AI. This should start with unifying existing data management systems into a single, standardized platform. To do this, we recommend a separate organizational body with policy coordination authority to enable efficient policy data exchanges across government ministries. Additionally, the public sector should build trust-based public-private partnerships to make up for its shortfall of technical expertise, using the private sector's capacity.
Despite high hopes for artificial intelligence (AI)
to generate powerful innovations
across the public sphere backed by its strong prediction skills,
Korea has not fully brought the technologies
into the public sector in tasks like identifying policy target groups
and managing follow-up tasks in line with its policy objectives.
to generate powerful innovations
across the public sphere backed by its strong prediction skills,
Korea has not fully brought the technologies
into the public sector in tasks like identifying policy target groups
and managing follow-up tasks in line with its policy objectives.
This study presents strategies and ideas for transforming
the public sector into a data-driven decision-making system
to enhance the effectiveness of policies in various fields.
According to the results of the machine learning model estimated using company characteristics and performance indicators, the machine learning model is useful in predicting sales growth.
Predictions on sales growth generated by the machine learning algorithm were applied to recipient firms, and the result confirms that using AI for selecting policy targets can enhance policy effectiveness.
In a KDI survey in 2020, respondents pointed out that what hinders transitioning into a system of AI-applied, data-driven policymaking in the public sector are:
1) incomplete standardization and linkage of policy information between governmental ministries and 2) lack of expertise in technology utilization in the public sector.
- Policy Recommendations for Data-driven Policymaking
- A national strategy should coordinate the transformation into a government as a platform enabling data-driven policymaking in the public sector. Public services, such as education, health care, public safety, national defense, and business support, take up a substantial share of the government budget and are in dire need of improving policy effectiveness.
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- Each public field can introduce AI technologies by way of designing a policy to support competent public institutions with the provision of assistance, not only technically, such as system, data platform, and security, but also in terms of workplace innovation, such as consulting on organizational diagnosis and reconstructing, education, training, etc.
- This study recommends detailed strategies to use AI to improve the effectiveness of support policy: 1) unifying the existing data management systems into a single platform, 2) reorganizing the government’s operating system to enable efficient exchanges of policy information, and 3) building a trust-based public-private partnership.
CONTENTS
- 1. Introduction
2. Adopting AI to Government Policy
3. Exploring the Benefits of AI Adoption in Selecting Policy Targets for SME Support Policy
4. Obstacles to the Transition into AIdriven Policymaking
5. Policy Recommendations for Data-driven Policymaking
- Summary
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■ Despite high hopes for artificial intelligence (AI) to generate powerful innovations across the public sphere backed by its strong prediction skills, Korea has not fully brought the technologies into the public sector in tasks like identifying policy target groups and managing follow-up tasks in line with its policy objectives.
- Recent cases of AI-applied public services in Korea show limited usage, mainly replacing simple repetitive tasks.
- Few leading countries are trying to apply AI-based analysis to select promising policy target groups to effectively achieve policy goals and follow up on the performance of public projects.
- While the existing management system for policy performance is mostly about ex-post assessment of project outcomes, the application of AI technologies signifies a shift to data-driven decision-making that uses ex-ante forecasts of policy effects.
■ An analysis of AI-applied recipient selection of small and medium enterprise (SME) policy support programs demonstrated the efficiency of AI in predicting the performance of beneficiary firms after the program and AI's potential to significantly improve the effectiveness of public support by providing helpful information in screening out unfit SMEs.
- Using firm-level data, this study applies machine learning to various public financing programs (subsidies or loans for SMEs) funded by the Ministry of SMEs and Startups and finds that AI helps predict the growth of recipient firms in the years following policy support.
- The application of AI in identifying fitting recipients likely to achieve intended objectives may increase project effectiveness.
■ In a KDI survey in 2020, respondents pointed out that what hinders transitioning into a system of AI-applied, data-driven policymaking in the public sector are: 1) incomplete standardization and linkage of policy information between governmental ministries and 2) lack of expertise in technology utilization in the public sector.
■ By developing a strategy to propel a transition into data-driven policymaking in the public sector, coordinated national-level efforts must be made to heighten policy effectiveness across different public fields, including education, health care, public safety, national defense, and business support.
- One way to adopt AI technologies in the public sector is by designing a policy to support technology adoption for competent public institutions. Support measures may cover system, data platform, security, organizational consulting, training, etc.
- Detailed strategies are: 1) unifying existing data management systems into one single platform, 2) reorganizing the way government work gets done to enable efficient exchange of policy information, and 3) building a trust-based public-private partnership.
- By examining the policy cycle from planning and implementation to evaluation, it is important to clarify areas for AI to contribute to policy decision-making. Also, the government needs step-by-step strategies toward data-driven policymaking, such as setting clear project objectives, selecting and sharing data, establishing system and security, and promoting operational transparency.
- Contents
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1. Introduction
2. Adopting AI to Government Policy
3. Exploring the Benefits of AI Adoption in Selecting Policy Targets for SME Support Policy
4. Obstacles to the Transition into AIdriven Policymaking
5. Policy Recommendations for Data-driven Policymaking
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