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Author: Minho Kim Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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.
Author: Minho Kim Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
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.
Author: El Bachir Boukherouaa Publisher: International Monetary Fund ISBN: 1589063953 Category : Business & Economics Languages : en Pages : 35
Book Description
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Author: Ajay Agrawal Publisher: University of Chicago Press ISBN: 0226833127 Category : Business & Economics Languages : en Pages : 172
Book Description
A timely investigation of the potential economic effects, both realized and unrealized, of artificial intelligence within the United States healthcare system. In sweeping conversations about the impact of artificial intelligence on many sectors of the economy, healthcare has received relatively little attention. Yet it seems unlikely that an industry that represents nearly one-fifth of the economy could escape the efficiency and cost-driven disruptions of AI. The Economics of Artificial Intelligence: Health Care Challenges brings together contributions from health economists, physicians, philosophers, and scholars in law, public health, and machine learning to identify the primary barriers to entry of AI in the healthcare sector. Across original papers and in wide-ranging responses, the contributors analyze barriers of four types: incentives, management, data availability, and regulation. They also suggest that AI has the potential to improve outcomes and lower costs. Understanding both the benefits of and barriers to AI adoption is essential for designing policies that will affect the evolution of the healthcare system.
Author: Committee on Technology National Science and Technology Council, Committee on Technology Publisher: Createspace Independent Publishing Platform ISBN: 9781540396518 Category : Languages : en Pages : 58
Book Description
Advances in Artificial Intelligence (AI) technology have opened up new markets and new opportunities for progress in critical areas such as health, education, energy, and the environment. In recent years, machines have surpassed humans in the performance of certain specific tasks, such as some aspects of image recognition. Experts forecast that rapid progress in the field of specialized artificial intelligence will continue. Although it is very unlikely that machines will exhibit broadly-applicable intelligence comparable to or exceeding that of humans in the next 20 years, it is to be expected that machines will reach and exceed human performance on more and more tasks. As a contribution toward preparing the United States for a future in which AI plays a growing role, this report surveys the current state of AI, its existing and potential applications, and the questions that are raised for society and public policy by progress in AI. The report also makes recommendations for specific further actions by Federal agencies and other actors.
Author: Miao, Fengchun Publisher: UNESCO Publishing ISBN: 9231004476 Category : Political Science Languages : en Pages : 50
Book Description
Artificial Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching and learning practices, and ultimately accelerate the progress towards SDG 4. However, these rapid technological developments inevitably bring multiple risks and challenges, which have so far outpaced policy debates and regulatory frameworks. This publication offers guidance for policy-makers on how best to leverage the opportunities and address the risks, presented by the growing connection between AI and education. It starts with the essentials of AI: definitions, techniques and technologies. It continues with a detailed analysis of the emerging trends and implications of AI for teaching and learning, including how we can ensure the ethical, inclusive and equitable use of AI in education, how education can prepare humans to live and work with AI, and how AI can be applied to enhance education. It finally introduces the challenges of harnessing AI to achieve SDG 4 and offers concrete actionable recommendations for policy-makers to plan policies and programmes for local contexts. [Publisher summary, ed]
Author: OECD Publisher: OECD Publishing ISBN: 9264545190 Category : Languages : en Pages : 152
Book Description
The artificial intelligence (AI) landscape has evolved significantly from 1950 when Alan Turing first posed the question of whether machines can think. Today, AI is transforming societies and economies. It promises to generate productivity gains, improve well-being and help address global challenges, such as climate change, resource scarcity and health crises.
Author: Minho Kim Publisher: ISBN: Category : Languages : ko Pages : 0
Book Description
English Abstract: 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.
Author: Ogunleye, Olalekan Samuel Publisher: IGI Global ISBN: 1668497182 Category : Political Science Languages : en Pages : 358
Book Description
In our data-rich era, extracting meaningful insights from the vast amount of information has become a crucial challenge, especially in government service delivery where informed decisions are paramount. Traditional approaches struggle with the enormity of data, highlighting the need for a new approach that integrates data science and machine learning. The book, Machine Learning and Data Science Techniques for Effective Government Service Delivery, becomes a vital resource in this transformation, offering a deep understanding of these technologies and their applications. Within the complex landscape of modern governance, this book stands as a solution-oriented guide. Recognizing data's value in the 21st century, it navigates the world of data science and machine learning, enhancing the mechanics of government service. By addressing citizens' evolving needs, these advanced methods counter inefficiencies in traditional systems. Tailored for experts across technology, academia, and government, the book bridges theory and practicality. Covering foundational concepts and innovative applications, it explores the potential of data-driven decision-making for a more efficient and citizen-centric government future.
Author: Adam Bohr Publisher: Academic Press ISBN: 0128184396 Category : Computers Languages : en Pages : 385
Book Description
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. - Highlights different data techniques in healthcare data analysis, including machine learning and data mining - Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks - Includes applications and case studies across all areas of AI in healthcare data
Author: Minho Kim
Author: Minho Kim
Author: El Bachir Boukherouaa
Author: Ajay Agrawal
Author: Committee on Technology National Science and Technology Council, Committee on Technology
Author: Miao, Fengchun
Author: OECD
Author: Minho Kim
Author: Ogunleye, Olalekan Samuel
Author: Alan Shark
Author: Adam Bohr