개인정보가 포함된 인공지능 학습데이터의 처리 근거 - 공개된 개인정보, 이미 보유한 이용자 개인정보를 중심으로

In the era of “big data”, personal information can be accumulated and utilized in large quantities. At the same time, the uneasiness about the invasion of personal privacy also follows. This paper focuses on the development and evolution of Japan’s Personal Information Protection Law, the restrictions on the application of personal information in Japan, the key points of revision of Japan’s Personal Information Protection Law and other measures taken by the Japanese government, and puts forward three inspirations from Japan’s personal information protection system to China. That is, (1) formulate a special law of the Personal Information Protection Law of the People’s Republic of China, (2) encourage all social parties to actively participate in “personal information protection”, and (3) establish a personal information protection system in line with national conditions. It is expected to provide useful reference for the further improvement of China’s information protection system.

With the rapid development of mobile Internet, minors' personal information is inevitably collected and processed by various information processors. Due to the characteristics of immature mental development and weak risk prediction ability of minors, it is difficult to effectively protect their personal information security. Although China's "Personal Information Protection Law" makes provisions on the protection of minors' personal information, there are still some problems such as "one-size-fits-all" age limit and imperfect guardian consent system. This paper will test the current hundreds of various types of apps, conduct a questionnaire survey on the protection of personal information of minors and their guardians, analyze the current dilemma of the protection of personal information of minors at the social level, propose countermeasures according to the symptoms, and explore more targeted ways to protect the rights and interests of minors' personal information. Escort the physical and mental healthy development of minors under the background of the information age.

In South Korea, artificial intelligence (AI)-related personal information protection has long been an issue, and the Lee-Luda (also referred to as ‘Iruda’) incident drove the development of specific policies and guidelines. The Lee-Luda case is fairly important for AI governance in Korea, since the Personal Information Protection Commission (PIPC) decision was the first to make a judgment on the Personal Information Protection Act (PIPA) violations as a result of development and operation of AI. Following the Lee-Luda case, Korean society became more aware of the risks posed by privacy infringement in the age of intelligent information technology. Lawmakers began to establish legal frameworks for the protection of personal information while developing AI products and services. This study reviews legal issues and suggests policy directions while highlighting the significance of the Lee-Luda case, which was an epochal turning point in the protection of personal information related to AI products and services in Korea. First, the authors review the PIPC’s decision on Scatter Lab’s violation of the PIPA, analyse the legal issues in the Lee-Luda case, and derive the significance and limitations of the decision. Next, the authors suggest that it is desirable for AI governance to move towards self-regulation in a manner that ensures the autonomy of AI developers and operators; they also review the recent trends in the laws and policies on data protection that have been continually developed since the Lee-Luda case, especially with a focus on the AI Personal Information Protection Self-Checklist (Self-Checklist).

The introduction of the Law of the People’s Republic of China on the Protection of Personal Information (hereinafter referred to as the Personal Information Protection Law) is a strong response to the unwavering defense of the right to self-determination of personal information in the era of the great development of China’s digital economy. There are three prerequisites for the application of the imputation principle of the Personal Information Protection Law: the information processing subject is the Personal Information Processor; the information right subject is a natural person; and the tort liability is the basis of the claim. Therefore, only by clarifying the above three premises, especially the dichotomy of processor and controller in the concept of personal information processor and the single theory of processor, can the liability principle be applied accurately.

Personal information has the attributes of personal dignity and personal interests. In the era of big data, the infringement of personal information is common. Although the introduction of the "Personal Information Protection Law" has improved the situation of personal information infringement to a certain extent, there are still lax administrative supervision and difficulties in administrative relief in the administrative protection of personal information. Therefore, it is necessary to improve the administrative supervision system and strengthen administrative relief measures to protect citizens' personal information.

Artificial intelligence is widely used in healthcare. World leading scientific schools are experimenting with various machine learning models and trying to implement them into medical practice. In various fields of medicine, there are good solutions already exists that allow artificial intelligence products use as medical devices for making medical decisions. For example, artificial intelligence products received a special impetus to this during COVID-19 in radiology. For high-quality artificial intelligence training, high-quality datasets are needed. That’s why as in general artificial intelligence, the transition from a model-centric approach to a data-centric approach is gradually moving. The data quality for neural networks training is becoming more and more acute. There are many problems associated with the statistically reliable medical datasets collection. The article considers an innovative solution of recent years - synthetic datasets, which can solve many dataset quality problems for artificial intelligence training. Key factors affecting the complexity of medical data mining are analyzed and examples of synthetic datasets can reduce artificial intelligence development time, balance data and improve the quality of artificial intelligence products are presented.KeywordsArtificial intelligenceSynthetic datasetsMedical artificial intelligence products

In the process of massive data collection and in-depth mining by generative artificial intelligence (AI). A large amount of personal information can be identified, posing numerous challenges in the field of personal information protection. Specifically, these challenges manifest as improper collection and abuse of personal information, increased risk of leakage, and expanded crises in group information security. Existing personal information protection rules struggle to adapt to the technical characteristics of generative AI, facing dilemmas such as the emasculation of the informed consent rule, difficulties in implementing the principle of data minimization, insufficient protection of personal information subjects’ rights, and blocked remedies. To address these issues, this paper proposes path optimization solutions based on a risk-based governance framework: (1) strengthening the full-process application and supervision of Personal Information Protection Impact Assessments (PIPIA), and constructing a scenario-based hierarchical risk governance system; (2) optimizing existing rules in combination with technical realities, improving the hierarchical consent mechanism, and promoting the risk-oriented interpretation of the data minimization principle; giving play to the risk prevention role of procuratorial public interest litigation, and clarifying applicable standards and initiation conditions. The research aims to balance the technological innovation of generative AI with the protection of personal information rights and interests, provide theoretical support for the improvement of relevant rules and promote the healthy and orderly development of generative AI.

Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogically structured frameworks that address non‐technical dimensions such as ethics, contextual understanding and the development of human‐centered, critical and lifelong learning mindsets to adapt to rapid technology changes. Furthermore, limitation of time in teacher education syllabus and faculty's lack of AI literacy require targeted intervention. This design‐based research (DBR) aims to fill this gap by developing and evaluating a set of design principles for GenAI literacy training in teacher education. Integrating contemporary AI competency frameworks for learners and teachers, the study implements these principles in a workshop that serves as the research prototype. The prototype was first piloted with 14 master's students and then evaluated with 29 teacher education (TE) students. Results show significant gains in participants' AI competence self‐efficacy, attitudes towards GenAI and commitment to critical, ethical and pedagogical engagement with GenAI tools. The findings highlight the need for teacher education programmes to integrate GenAI literacy that supports teachers' evolving roles as reflective practitioners, co‐creators and lifelong learners in an AI‐driven world. Practitioner notes What is already known about this topic Students' self‐efficacy and mindset influence their adoption of GenAI in learning and teaching. GenAI competency is essential for teacher education students, encompassing technological, ethical and sociocultural dimensions. Implementing AI literacy training faces challenges such as educators' limited AI literacy, the lack of pedagogical frameworks and the need to integrate technical knowledge with ethical reasoning, self‐regulated learning and metacognitive skills. Transformative learning is crucial in preparing teacher education students as co‐creators and facilitators in AI‐enhanced education. What this paper adds This study presents the design and evaluation of an AI competency training programme to enhance teacher education students' self‐efficacy, critical and human‐centered AI mindsets and human–AI interaction skills. Using design‐based research, it identifies key principles for AI literacy training, demonstrating how transformative, active and experiential learning approaches can prepare teacher education students for evolving roles in AI‐enhanced education. Implications for practice and/or policy Educational technologists can apply the proposed design principles to develop GenAI tools and AI systems that enhance AI competency and self‐regulated learning (SRL). Educators can integrate these AI training principles into their teaching strategies and enhance their task assignments and assessments to foster deep learning, self‐regulated learning and metacognitive skills in the age of GenAI. Policymakers should update teacher education curriculum to include AI competency, strengthen content and pedagogical knowledge, especially in social emotional learning, formative and authentic assessment methods and establish guidelines for selecting GenAI tools that support human intelligence and mitigate over‐reliance.

The rights of data subjects regarding their personal information are aimed at protecting the right to self-determination over personal information, which is a type of personality right. Regarding ‘pseudonymized information,’ which is a type of personal information, lower court rulings and the Supreme Court ruling reached different conclusions in a case where mobile communication service users requested that telecommunications companies refrain from pseudonymizing their personal information for the purpose of applying the special provisions on processing pseudonymized information under Article 28-2 of the Personal Information Protection Act, namely for statistical purposes, scientific research purposes, and archiving purposes in the public interest. The essence of the first-instance and second-instance rulings was that while data subjects cannot exercise the right to request suspension of processing for information that has already been pseudonymized (i.e., pseudonymized information), they do possess the right to request prior suspension of pseudonymization. However, the Supreme Court ruled that if pseudonymized information falls under the special provisions governing its processing, the data subject is not entitled to the right to request suspension of pseudonymization under Article 37 of the Act, regardless of whether the information has already been pseudonymized or not. In my view, the Personal Information Protection Act conceptually distinguishes between ‘pseudonymization’ and ‘processing of pseudonymized information,’ and Article 28-2 of the Act, a special provision concerning the processing of pseudonymized information, regulates ‘processing of pseudonymized information.’ Therefore, Article 28-7 of the Act, which deprives data subjects of their right to request suspension of personal information processing as stipulated in Article 37, does not apply to pseudonymization itself. Therefore, the conclusions of the first-instance and second-instance judgments in the case — that the data subject retains the right to request suspension of pseudonymization — are considered reasonable interpretations under the current Personal Information Protection Act. However, regarding the utilization of pseudonymized information, for the Personal Information Protection Act to actively respond to the need for fostering new data-related industries and the industrial sector's requirement for data usage, as considered in the Supreme Court's ruling on the Case, it is necessary to establish technical safeguards against the risk of re-identification of pseudonymized information and to enact legislative supplements based on societal consensus regarding the restriction of data subjects' exercise of their right to self-determination over their personal information in relation to pseudonymization.

As China rapidly advances in AI innovation and development, especially in frontier AI, its regulatory and ethical frameworks are under increasing pressure to ensure that technological progress aligns with human interests and societal values. This Article argues that AI value alignment—the process of ensuring AI systems act in accordance with human values, norms, and ethical principles—should be adopted as a strategic pillar in China’s evolving AI governance architecture. While China has already established a comprehensive legal, ethical, and self-regulatory landscape to address AI risks, these mechanisms often rely on reactive enforcement and external compliance. In contrast, AI value alignment offers a proactive, intrinsic approach that embeds safety and ethical constraints directly into AI systems, making them safer, more trustworthy, and responsive to human needs. This study begins by mapping China’s current AI governance landscape, including national legislation such as the Cybersecurity Law, Personal Information Protection Law, and a growing set of regulations targeted at algorithms and generative AI. It also evaluates China’s normative commitments, such as the “human-centric” and “tech for good” principles articulated in national policy documents, and the increasing role of corporate self-regulation among major technology firms. While commendable in scope and ambition, these governance mechanisms often fall short in ensuring that AI behavior aligns with safety constraints and ethical intent—particularly when AI systems (such as agentic AI) become more autonomous and capable. This gap highlights the urgent need for a systematic value alignment strategy. The Article then delves into the conceptual and technical foundations of AI value alignment, identifying both engineering challenges—such as reward misspecification, data bias, and model deception—and normative dilemmas, including moral pluralism, value aggregation, and dynamic ethics. Special attention is paid to frontier models like large language models and artificial general intelligence (AGI), which pose alignment challenges at a scale previously unseen. Drawing on contemporary alignment techniques such as RLHF (Reinforcement Learning from Human Feedback) and principle-based alignment, such as Anthropic’s Constitutional AI, the Article explores their limitations and calls for a more diversified, interdisciplinary, and forward-looking alignment research agenda. Finally, the Article offers a roadmap for operationalizing AI value alignment across three key governance domains: Law and regulation, ethical norms, and industry self-regulation. Recommendations include the incorporation of alignment assessments into regulatory filings, the development of technical standards for value alignment and ethics-by-design guidelines, and institutional investments in safety and alignment research. The Article concludes by asserting that value alignment is not merely a technical safeguard but a governance imperative for the age of autonomous AI and agentic AI. By integrating alignment into its AI governance strategy, China can not only enhance domestic safety and public trust but also better coordinate with global AI ethics and safety initiatives—ultimately contributing to the shared goal of human-aligned and beneficial artificial intelligence.

The Personal Information Protection Law (PIPL) was implemented in November 2021 to safeguard the personal information rights and interests of Internet users in China. However, the impact and existing shortcomings of the PIPL remain unclear, carrying significant implications for policymakers. This study examined privacy policies on 13 online platforms before and after the PIPL. Concurrently, it conducted semi-structured interviews with 30 Chinese Internet users to assess their perceptions of the PIPL. Users were also given tasks to identify non-compliance within the platforms, assessing their ability to address related privacy concerns effectively. The research revealed various instances of non-compliance in post-PIPL privacy policies, especially concerning inadequate risk assessments for sensitive data. Although users identified some non-compliant activities like app eavesdropping, issues related to individual consent proved challenging. Surprisingly, over half of the interviewees believed that the government could access their personal data without explicit consent. Our findings and implications can be valuable for lawmakers, online platforms, users, and future researchers seeking to enhance personal privacy practices both in China and globally.

A power plant converts energy from a non-electric form to an electric one. Depending on the energy conversion, power plants are classified as minerals, nuclear, solar, geothermal, hydroelectric, and so on. The main goal is to perform this transformation as best as possible. Criteria of safety, efficiency, reliability and affordability are taken into account as benchmarks. The station consists of several units that generate and work together to meet electricity needs. For a fossil fuel power plant, each unit consists of three main components: a boiler, a turbine and a generator. The complexity of the operation is due to the variability of the load and high efficiency required in a wide range of operations. The main difficulties for the management task then arise due to the strong link between process variables and process nonlinearity. The purpose of this article is to analyze and develop a proposal for the introduction of artificial intelligence in the power industry of Ukraine. To achieve this goal, we will offer the following tasks: - analyze existing systems for the use of artificial intelligence in industry; - develop proposals for the introduction of artificial intelligence in the electricity system of Ukraine. In this study, methods of statistical, factorial, historical, comparative, logical, economic-mathematical and systems analysis, the method of expert assessments were used, which allowed to formulate proposals for the introduction of artificial intelligence in the power industry. Artificial neural networks are the first step towards a fundamentally new system of information analysis. They are mathematical and computer models that simulate the work of biological neurons, ie a system of interacting processes, built on the principle of connecting nerve cells of the human brain. It should be noted that they differ from conventional machine algorithms in the ability to learn, memorize and reproduce images, determine patterns, memorize and analyze information and obtain results. Output signals that arrive at the next processor and continue to be converted. Thus, artificial neurons form networks and layers in which connections are created, restored, maintained and destroyed by special signals. With regard to artificial intelligence, there is no generally accepted definition of artificial intelligence, therefore, by artificial intelligence we mean a machine system capable of learning using objective knowledge and experience, to solve creative problems like the human brain and inventive tasks, not going through options, build strategies and apply abstract concepts. Digitalization and artificial intelligence are penetrating most sectors of the economy, including the electricity sector. The digitalization of energy requires the development and widespread use of end-to-end technologies, including industrial Internet, components of robotics, wireless communications, artificial intelligence and others. Thus, artificial intelligence technologies have prospects for development. Of course, significant targeted funding is needed to achieve significant results. The interaction of research institutes and universities with business is very important, where artificial intelligence technologies are also being developed. Attention of the authorities and society, their understanding of the importance of the tasks facing countries to achieve the goal of implementing artificial intelligence. With regard to electricity, the integration of artificial intelligence into the industry will help optimize and increase efficiency in all aspects of energy production, transmission and consumption. It should be noted that the development of electricity is a step towards the development of other industries. That is why the transition to the digital industry is impossible without the digitalization and intellectualization of the power industry

The rapid growth of the big data industry has made personal information the basic resource. However, the convenience of obtaining personal information and the commerciality will inevitably lead to the problem of personal information security protection. Based on this background, this paper takes company A as the research case draws out the insufficiency of the protection of customers personal information: system risk, human risk, and process management risk in data storage and transmission, and systemic and human factors in data-supported security, and puts forward the countermeasures for the protection of personal information: 1) Clarification of the main bodys responsibility; 2) Classification and grading control; 3) Avoiding sensitive data out of the network; and 4) Information use requiring authorization. This study provides relevant suggestions on how to improve the security protection of personal information in the field of big data applications; from the technical aspect, it adopts classification control use authorization and other technologies to reduce network risks; on the other hand, from the management aspect, it clarifies the main safety responsibility, strengthens the professional skills training of personnel, and authenticates the use within the scope of authority, which can help enterprises make future strategies.

With the advent of artificial intelligence (AI), AI proficiency has become vital for teachers and learners in educational settings. Although preservice teachers acknowledge AI’s educational potential, they also express concerns regarding the technical challenges and integration of AI in teaching. In response, the Ministry of Education has mandated curriculum revisions in national universities to enhance AI and digital textbook competencies among preservice teachers. This study proposes to develop a program within university curricula aimed at developing the AI competencies of preservice teachers in the field of art education. Leveraging the curriculum of G National University of Education, elements from the Ministry’s “2024 Teacher Qualification Examination Practical Handbook” were incorporated into the Art Education I and II courses. Based on “AI·Digital Competency Structure for Art Teachers,” Art Education I—a required course for sophomores—integrates AI and digital competencies in 8 of its 15 sessions. The targeted sub-competencies include basic AI knowledge, digital device use, understanding social and educa tional implications, problem-solving, digital etiquette, protection of personal information and copyright, and evaluation of digital information in art.

개인정보자기결정권은 헌법전에 명문으로 규정되지 않았음에도 불구하고 헌법해석에 의해 기본권으로 인정되는 기본권이다. 그리고 개인정보자기결정권이라는 기본권은 정보화시대에 정보주체의 사생활 보호 등 여러 보호영역과의 관련성을 갖는 중요한 기본권이다. 그런데 메르스 사태 이후 현재의 코로나19 상황에 이르기까지 감염병 예방과 전파방지의 과정 속에서 개인정보의 제한과 침해에 대한 숙고와 검토가 필요하다는 것이 자명해졌다. 왜냐하면 실제로 코로나 19에 대한 효과적 대응을 위한 정보공개 시스템 실행의 이면에는 확진환자 개인의 사생활이 여과 없이 공개되는 부작용 발생이 적지 않았기 때문이다. 이미 국가인권위원회도 확진자의 과도한 사생활 노출로 인한 인권침해가 발생하지 않도록 해야 한다고 권고한 바 있다. 이러한 상황에 대한 문제의식을 바탕으로 이 글에서는 개인의 민감정보 등 개인정보 공개와 관련한 개인정보 보호법과 감염병 예방법의 관련 규정들을 상관적으로 검토하였다. 특히 개인정보자기결정권과 관련하여 감염병 예방법상의 정보공개와 관련된 규정인 제34조의2 규정을 중심으로, 확진자의 내밀한 사생활을 보호할 수 있는 세부적이고 합리적인 공개기준의 정립 여부와 특히 위치정보 등의 공개로 인한 권리침해의 문제에 대해 살펴 보았다. 일반적인 개인정보와 구분되는 개인위치정보의 특성을 고려할 때 개인위치정보가 개인정보 보호법상의 민감정보에 해당될 수 있는가, 개인위치정보의 분류에 관하여 온라인행태정보유형으로 생존하는 개인에 관한 정보로써 개인정보 가명가공정보익명가공정보 중 어디에도 해당하지 않는 정보유형으로 새로이 “개인관련정보”를 입안하는 방안의 검토, 감염병 예방법상 개인위치정보를 비롯한 개인정보를 제공하고 공개함으로써 발생될 수 있는 권리침해에 관한 대책과 절차에 대해 검토하였다. 개인정보에 관련된 법제는 그 정보의 보호와 이용이 동시에 고려되어야 할 필요가 있다. 즉 개인정보 관련법제는 ‘개인정보의 유용성 배려’와 ‘개인의 권리이익 보호’의 균형을 맞추는 병존적 구조를 반영한 것이어야 할 것이다. 다만 양법익 간의 관계에 있어서는 ‘개인의 권리이익 보호’가 우선적으로 고려되는 것이어야만 함은 물론이다. 안전사회 추구로 인한 부작용을 간과해서는 안되는 것도, 개인정보의 유통으로 인한 비재산적 손해에 대한 배상책임을 고민해야 하는 것 도 이러한 헌법정신에 기인한다고 하겠다. 감염병 예방법의 새로운 수정이 시작되어야 한다.The right to self-determination of personal information is a basic right recognized as a basic right by constitutional interpretation, even though it was not prescribed in the Constitution. And this right is an important basic right that has relevance to various areas of protection, such as protection of the privacy of the information subject in the information age. However, from the MERS outbreak to the current COVID-19, it has become apparent that it is necessary to consider and review the restrictions and infringements of personal information in the process of preventing infectious diseases and preventing transmission. This is because, in fact, behind the implementation of the information disclosure system for effective response to COVID-19, there were not a few occurrences of side effects in which the personal life of confirmed patients was disclosed without filtering. The National Human Rights Commission of Korea has already recommended that human rights violations should not occur due to excessive exposure of the confirmed person s private life. Based on the awareness of the problem in this situation, in this article, the related regulations of the Personal Information Protection Act and the Infectious Disease Control and Prevention Act related to the disclosure of personal information, such as personal sensitive information, were reviewed correlatively. In particular, with respect to the right to self-determination of personal information, detailed and reasonable disclosure standards have been established to protect the confidential personal life of the confirmed person, with a focus on Article 34-2, which is a regulation related to the disclosure of information in the Infectious Disease Prevention Act, and, in particular, disclosure of location information, etc., I reviewed the problem of infringement of rights caused by. Considering the characteristics of personal location information that are distinguished from general personal information, can personal location information correspond to sensitive information of the Personal Information Protection Act?, review of a plan to devise a new “personal-related information” as an information type, review measures and procedures for rights infringement that may occur by providing and disclosing personal information including personal location information in the Infectious Disease Control and Prevention Act. Legal systems related to personal information need to consider the protection and use of the information at the same time. In other words, the legislation related to personal information should reflect the coexistence structure that balances “consideration of the usefulness of personal information” and “protection of individual rights and interests”. However, in the relationship between the two legal interests, ‘protection of individual rights and interests’ should be given priority. It can be said that this constitutional spirit should not overlook the side effects caused by the pursuit of a safe society and the need to consider liability for compensation for non-property damages caused by the distribution of personal information. A new modification of the Infectious Disease Control and Prevention Act must be initiated.