Artificial intelligence (AI) or big data analysis are increasingly applied in very diverse fields such as marketing, healthcare, management, manufacturing, economic policy, not to mention professional sports. As available data explodes and AI technologies advance fast, the combination of these two is already transforming businesses and our lives. The goal is to augment human intelligence to be more accurate in detection, prediction, or decision-making. AI technologies offer new possibilities for policy-making and public administration helping the governments become more data-driven. In this respect, the combination of AI technology and government policy is an inevitable option to greatly increase the efficiency and effectiveness of the public service. At this point, the discussions for the successful introduction of AI technology in the public sector and policy-making are very urgent.

In this study, we explore the possible benefits of AI technology in the public sector by clarifying the connections and distinctions between AI technology and the existing evidence-based policy-making process. Besides, we identify the factors that hinder the introduction of the technology and suggest policy recommendations to overcome the challenges. We intend to provide information on how to build a government system for data-driven decision-making.

Chapter 2 argues that to reap the benefits of AI, it is necessary to change the current policy-making process to be more evidence-based. A fundamental transformation must be accompanied in the government system by reviewing the overall decision-making process including practices in collecting, managing, and sharing data. This chapter studies the development and application of evidence-based policies in Korea and other major countries and discusses the role of AI technology. Chapter 2 also deals with the possibilities and constraints when implementing AI technology in public policies especially in selecting policy targets.

Chapter 3 empirically presents the benefit of using AI technology in business support policy. It presents methods and results of applying machine learning, an AI technology, to the actual Korean government’s business support policies. We discuss important issues in the actual implementation of machine learning in policy targeting. The Korean government is implementing hundreds of business support policies to enhance corporate competitiveness and strengthen the overall competitiveness of the industry. The success of a business support policy can largely depend on the selection of a company that meets the purpose of support. This chapter discusses ways to improve the effectiveness of government support programs by comparing the results of selected targets by machine learning with the actual recipients of the programs.

The biggest advantage of AI technology lies in its infinite scalability. The advantage can take place when policy-makers use relevant data and the results from the analysis to improve the policy. The government holds a vast amount of personal and corporate information through administrative data. When the information is combined with private big data, the advantage becomes even greater. Besides the business support policy, AI technology can significantly increase policy effectiveness when it is applied to education, medical care, policing and defense to name a few. Still, we face the gap between the possibility and reality in the AI application. In Chapter 4, we conducted a survey on the perception and actual status of the introduction of artificial intelligence into the policy-making process with public sector workers related to business support policy and business people who are beneficiaries of the policy. The survey results showed that the respondents were recognizing the positive aspect of the application of AI in terms of enhancing fairness and reliability in selecting the policy targets. We identified technical, institutional, and cultural implementation barriers through the survey and suggested recommendations to overcome the barriers and increase the effectiveness of business support policies. The policy recommendations include a suggestion to form an organization that can oversee data management and artificial intelligence-related tasks scattered across various ministries. The organization is necessary to reinforce the strategic management capacity of information assets at the national level.

In conclusion, chapter 5 suggests that the current Korean national strategy for AI should include a data-driven government strategy across the public sector. The data-driven government strategy should prioritize activities in transforming governments’ decision-making process to be data-driven or evidence-based. The chapter ends with a four-step guide to a data-driven organization.

Preface
Executive Summary

Chapter 1 Introduction
　References

Chapter 2 Evidence-Based Policy-Making and AI Technology
　Section 1 Establishing Evidence-Based Policies
　Section 2 AI Technology and Public Services
　Section 3 Data-Driven Policy-Making Using AI Technology
　References

Chapter 3 AI-Based Business Support Policies
　Section 1 Application of AI Technology to Business Support Policies
　Section 2 Application of AI Technology to SME Funding Programs
　Section 3 Results of AI Application to Support Programs
　Section 4 Summary and Implications
　References

Chapter 4 Barriers and Solutions for Adopting AI-Based Business Support Policies
　Section 1 Perception and Survey on AI Adoption in Business Support Policies
　Section 2 Factors Influencing AI Application in Business Support Policies
　Section 3 Implications and Policy Recommendations
　References

Chapter 5 Conclusion
　References

Appendix
ABSTRACT