Проблема определения субъектов юридической ответственности за вред, причиненный искусственным интеллектом: анализ путей решения

Objective: to formulate proposals to form a system of subsidiary liability for harm resulting from the use of artificial intelligence systems. Methods: the research is based on a comprehensive methodological basis, including the abstract logical method for theoretical understanding of the legal nature of artificial intelligence as an object of legal regulation; the method of comparison to analyze the Russian and European legislations on tort liability; generalization to systematize the existing concepts of responsibility distribution between subjects of law; and correlation analysis to identify the relationships between the typology of artificial intelligence systems and the mechanisms of legal responsibility for their functioning. Results: the study summarizes and systematizes modern theoretical and legal concepts and regulations of the European Union and the Russian Federation on the distribution of subsidiary responsibility for the adverse effects of artificial intelligence. Potential subjects of responsibility were identified, as well as the key factors influencing the distribution of responsibility between them. A multidimensional matrix was developed for responsibility distribution between the subjects, taking into account their impact on the specific artificial intelligence system functioning and the systems typologization under the risk-based approach. Scientific novelty: for the first time, an original concept is proposed, which combines the differentiation of the subjects’ roles in terms of their real impact on the artificial intelligence results; the differentiation of artificial intelligence systems under the risk-based approach; and the system of legal presumptions of responsibility distribution corresponding to the above two classifications. The novelty lies in the creation of a multidimensional matrix of subsidiary liability, which allows taking into account many factors when determining the subject of responsibility in each specific case of harm caused by artificial intelligence systems, which differs significantly from existing unilateral approaches to this issue. Practical significance: the research conclusions and suggestions can be used to develop the doctrine of subsidiary responsibility in the field of artificial intelligence use, to develop and modify the legal norms regulating artificial intelligence. The proposed multidimensional matrix of responsibility distribution can serve as a theoretical basis for improving judicial practice in cases of compensation for damage caused by artificial intelligence systems, as well as for creating an effective balance between stimulating the development of AI technologies and ensuring the protection of the rights and legitimate interests of individuals and legal entities.

The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps regarding how to implement and best use AI to add value to mental healthcare services, providers, and consumers. The aim of this paper is to identify challenges and opportunities for AI use in mental healthcare and to describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. The paper is based on a selective review of articles concerning AI in mental healthcare and implementation science. Research in implementation science has established the importance of considering and planning for implementation from the start, the progression of implementation through different stages, and the appreciation of determinants at multiple levels. Determinant frameworks and implementation theories have been developed to understand and explain how different determinants impact on implementation. AI research should explore the relevance of these determinants for AI implementation. Implementation strategies to support AI implementation must address determinants specific to AI implementation in mental health. There might also be a need to develop new theoretical approaches or augment and recontextualize existing ones. Implementation outcomes may have to be adapted to be relevant in an AI implementation context. Knowledge derived from implementation science could provide an important starting point for research on implementation of AI in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research. However, when taking advantage of the existing knowledge basis, it is important to also be explorative and study AI implementation in health and mental healthcare as a new phenomenon in its own right since implementing AI may differ in various ways from implementing evidence-based practices in terms of what implementation determinants, strategies, and outcomes are most relevant.Plain Language Summary: The implementation of artificial intelligence (AI) in mental healthcare offers a potential solution to some of the problems associated with the availability, attractiveness, and accessibility of mental healthcare services. However, there are many knowledge gaps concerning how to implement and best use AI to add value to mental healthcare services, providers, and consumers. This paper is based on a selective review of articles concerning AI in mental healthcare and implementation science, with the aim to identify challenges and opportunities for the use of AI in mental healthcare and describe key insights from implementation science of potential relevance to understand and facilitate AI implementation in mental healthcare. AI offers opportunities for identifying the patients most in need of care or the interventions that might be most appropriate for a given population or individual. AI also offers opportunities for supporting a more reliable diagnosis of psychiatric disorders and ongoing monitoring and tailoring during the course of treatment. However, AI implementation challenges exist at organizational/policy, individual, and technical levels, making it relevant to draw on implementation science knowledge for understanding and facilitating implementation of AI in mental healthcare. Knowledge derived from implementation science could provide an important starting point for research on AI implementation in mental healthcare. This field has generated many insights and provides a broad range of theories, frameworks, and concepts that are likely relevant for this research.

Artificial intelligence (AI) is increasingly integrated into radiology workflows, supporting tasks such as image reconstruction, patient positioning, and administrative processes. While these developments promise efficiency and improved diagnostic quality, their impact on radiographers' professional roles remains unclear, particularly in Sweden, where formal AI training is absent, and staffing shortages persist. Understanding radiographers' perspectives is essential for safe implementation and workforce sustainability. This study aims to explore how the professional role of radiographers is influenced by the current and future implementation of AI in radiology. A qualitative multi-case study was conducted across three Swedish university hospitals. Semi-structured interviews were carried out with 18 strategically selected participants, including section leaders, researchers, and department managers. Data were analysed using thematic analysis, focusing on perceptions of AI, its impact on professional identity, and anticipated future tasks. Five themes emerged in each of the 18 cases and were compared across hospitals. Participants reported a lack of training and uncertainty about the future, as radiographers' roles are rarely considered in technology implementation. Maintaining competence and professional development was highlighted as critical when introducing AI. AI is present in workflows such as image reconstruction, protocol optimisation and patient positioning, but formal training remains limited. Participants viewed AI as both a facilitator of daily work and a potential threat to core tasks, raising concerns about deskilling and loss of identity. Maintaining responsibility for patient safety was emphasised. Thoughtful implementation, structured training, and inclusive planning could enable radiographers to assume advanced roles, including oversight of AI outputs. Including radiographers in the implementation of AI models and formalising AI education, both in undergraduate education and postgraduate courses, are essential for strengthening the profession.

ObjectivesTo explore the views of intensive care professionals in high-income countries (HICs) and lower-to-middle-income countries (LMICs) regarding the use and implementation of artificial intelligence (AI) technologies in intensive care units...

The article examines the essence, classifies and highlights the historical aspects of the development of artificial intelligence technologies, identifies the trends and outlines the prospects for using the potential of modern information technologies in various spheres of public life. It is substantiated that the application of the capabilities of artificial intelligence-based systems in accounting practice makes it possible to automate certain accounting operations, in particular, processing and analysis of large amounts of accounting information, verification of accounting data and detection of errors, prevention of fraud, and also saves material and human resources for accounting, and increases the efficiency of the accounting process in general. Despite the significant advantages of integrating artificial intelligence into the accounting process, it is established that the use of the intelligent information technologies has risks for the accounting industry, which are associated with the release of a significant number of jobs, insufficient confidentiality and security of accounting data, the complexity of implementing an intelligent accounting system, and the lack of regulatory regulation of the procedure for using artificial intelligence systems in the accounting process. It is determined that the potential of artificial intelligence-enabled systems is gradually being used in accounting and this process will only intensify due to the rapid development of information technology and the trend towards automation of business processes, but there is still no talk of full integration of artificial intelligence into accounting practice. In order to ensure widespread implementation of artificial intelligence in the field of accounting, the author proposes a set of measures which include cultivation of a positive attitude towards intelligent information technologies, development of a national policy and strategy for implementation of artificial intelligence systems, professional development of practitioners, and development of information systems for automation of accounting based on artificial intelligence technologies.

The study is devoted to the definition of artificial intelligence as a legal category. At the same time, the peculiarity of the term and its general social significance determine the fact that in order to find an answer to the question of the legal nature of artificial intelligence, it is relevant to analyze not only purely legal scientific ideas, but also philosophical, psychological, social, religious and other aspects of understanding artificial intelligence and the impact of this phenomenon on various spheres of public life. In order to define the concept of artificial intelligence, the author examined the attempts to define it that have already been used in legislation. In particular, the author analyzed the Artificial Intelligence Act (AI Act), which entered into force in the EU on August 1, 2024, and concluded that this law does not answer the question of the subjectivity or lack thereof of artificial intelligence. Considerable attention is paid to the legal framework of Ukraine, in particular, the Resolution of the Cabinet of Ministers of Ukraine No. 1556-p of December 2, 2020, which approved the Concept of Artificial Intelligence Development in Ukraine. This policy document uses the basic principles of the Guidelines of the Organization for Economic Cooperation and Development (OECD) on Artificial Intelligence (Recommendation of the Council on Artificial Intelligence), which Ukraine joined in 2019. The definition of artificial intelligence as a legal category in legal doctrine encounters a number of fundamental problems, but they all have a common denominator in identifying the legal nature of the bearers of such intelligence. The analysis of modern legal doctrine has revealed general approaches to understanding the relationship between the use of artificial intelligence and its liability for actions, namely: 1) positioning of robots with artificial intelligence as an object of social relations (under this approach, robots with artificial intelligence are perceived only as possible assistance in social relations where the subjects are individuals and legal entities) 2) positioning of artificial intelligence robots as separate subjects of legal relations (under this approach, artificial intelligence robots are perceived as separate independent subjects of social relations with the ability to realize and assess the significance of their actions and actions of other persons relatively independently and to a sufficient extent). Based on the study, the author offers her own solution to the problem of determining the legal status of artificial intelligence (robot, intellectual agent). First of all, the author proves the need to gradually prepare the legal system for the emergence of a new subject - an electronic person. It is timely to discuss the practical possibility of granting in the future the status of a quasi-legal entity to the most advanced artificial intelligence systems with a high degree of autonomy, since it does not fall under the category of a legal entity (which is also a fiction). It is substantiated that electronic persons should be understood as powerful artificial intelligence systems endowed with the status of a "quasi-legal" person having an appropriate scope of special legal capacity depending on their functional purpose and capabilities. The study draws historical and legal parallels with the experience of Ancient Rome in terms of using (involving) phenomena which were not originally subjects of law and then acquired such a possibility (municipalities, institutions, slaves, etc.) to participate in civil circulation. Recognition of artificial intelligence robots ("electronic persons", "intellectual agents") as a quasi-legal entity will entail a number of additional legal consequences. In particular, there is a need to include "cyber capacity" in the list of types of legal personality of a legal entity, i.e. the ability to be an active participant in relations in the IT sphere (to enter into contracts as a user, to be a member of social networks, to participate in interactive campaigns, etc.) Cyber capacity can be realized through both transactions and legal acts. As with legal entities, electronic entities should be subject to mandatory state registration in the relevant electronic registers. At the same time, a system of licensing the types of activities of such entities and establishing standards and norms to which such entities must comply, depending on the type of activity, is also possible. In addition to the development of the necessary regulations and standards, it is very important to use soft law. In this case, ethical standards of artificial intelligence are its value basis - they must be observed by all participants in legal relations: both private companies and executive authorities. The author argues that an electronic person can be defined as a set of technologies which are recognized by law as a participant in property and non-property relations. Such a person has the legal status of a quasi-legal entity, is registered in accordance with the procedure established by law, and has a special legal personality depending on the functional purpose (field of activity).

Artificial intelligence (AI) implementation within the National Health Service (NHS): the South West London AI Working Group experience

Introduction: Artificial intelligence (AI) is the fascinating result of the convergence of various technologies, algorithms and approaches. Its role in early detection and diagnosis will be a boon to society in the next few years and should be seen as 'Augmented Intelligence.' Issues like ethical considerations and collaborations need to be sorted out to ensure apt implementation in healthcare. This article reviews the current status of AI in developing nations like India and highlights some insights that could provide new directions and opportunities for AI in healthcare, especially in the field of radiology. Implications for Patient Care: (1) AI will aid in nation-wide research and cooperation that will provide an impetus for the development of imaging science and decentralization of medical services. (2) AI may help to bridge the gap for need of specialized medical personnel in the peripheral areas in developing countries like India. (3) Government initiatives, ethical considerations and joint public private sector collaborations will ensure smooth transition and implementation of AI in healthcare especially in radiology. Summary Statement: This manuscript highlights the existing gap between the developed and developing nations regarding implementation and acceptance of AI in healthcare and at the same time brings out insights in regard to the government initiatives to ease the progress of AI in healthcare in developing countries like India.

In today's rapidly evolving business landscape, Artificial Intelligence (AI) stands as the proverbial 'elephant in the room,' profoundly shaping diverse sectors and contexts. While debates rage among policymakers, practitioners, and politicians about regulating AI's widespread use, it's undeniable that AI represents a long-awaited digital technology poised to revolutionize organizational performance. Amidst the post-COVID era, the allure of AI has intensified, yet the critical question lingers: Can firms effectively harness AI and other technologies, such as Generative AI and Large Language Models (LLMs), to enhance their existing systems? Through a systematic literature review, a clear correlation between firms’ implementation of AI, including the cutting-edge Generative AI, and their ability to adapt to changing market dynamics, drive operational excellence, and unlock new avenues for growth are explored. Moreover, key drivers of AI and digital adoption, such as the imperative for data-driven decision-making, the quest for customer-centricity, and the drive for sustainable business practices are explored. Our research not only highlights the transformative potential of AI and digital technologies but also provides actionable insights for business leaders navigating the complexities of technology adoption. By understanding the motivations, challenges, and strategic imperatives driving firms' technology choices, including the integration of Generative AI and LLMs, organizations can chart a path towards sustainable growth and competitive advantage in the digital age. This study underscores the revolutionary impact of Generative AI in digital transformation, offering a comprehensive understanding of their role in shaping the future of business.

To evaluate and understand pregnant patients' perspectives on the implementation of artificial intelligence (AI) in clinical care with a focus on opportunities to improve healthcare technologies and healthcare delivery. We developed an anonymous survey and enrolled patients presenting to the labor and delivery unit at a tertiary care center September 2019-June 2020. We investigated the role and interplay of patient demographic factors, healthcare literacy, understanding of AI, comfort levels with various AI scenarios, and preferences for AI use in clinical care. Of the 349 parturients, 57.6% were between the ages of 25-34years, 90.1% reported college or graduate education and 69.2% believed the benefits of AI use in clinical care outweighed the risks. Cluster analysis revealed 2 distinct groups: patients more comfortable with clinical AI use (Pro-AI) and those who preferred physician presence (AI-Cautious). Pro-AI patients had a higher degree of education, were more knowledgeable about AI use in their daily lives and saw AI use as a significant advancement in medicine. AI-Cautious patients reported a lack of human qualities and low trust in the technology as detriments to AI use. Patient trust and the preservation of the human physician-patient relationship are critical in moving forward with AI implementation in healthcare. Pregnant individuals are cautiously optimistic about AI use in their care. Our findings provide insights into the status of AI use in perinatal care and provide a platform for driving patient-centered innovations.

In the context of rapid digital transformation, artificial intelligence (AI) technologies implementation is becoming crucial for e-commerce competitiveness. According to Eurostat, in 2024, 13.48% of EU enterprises use AI technologies, showing growth from 8.0% in 2023. Given Ukraine's European integration aspirations, there is a need to study and adapt EU experience in implementing AI in e-commerce. The research aim is to analyze transformation processes in e-commerce systems under AI technologies influence, study EU experience, and identify development directions for Ukraine. Methodology is based on a systematic approach combining: analysis of relationships between e-commerce components, statistical analysis of quantitative data, comparative analysis of EU and Ukraine indicators, and economic modeling to assess AI implementation effects. Analysis revealed differentiation in AI implementation across the EU by enterprise size (from 11.21% for small to 41.17% for large companies) and countries (leaders - Denmark (27.58%), Sweden (25.09%), Belgium (24.71%)). In Ukraine, AI implementation is 6.9%. Priority areas identified: text processing (26.9%), speech generation (21.2%), and speech recognition (18.7%). Recommendations for phased AI implementation were developed considering enterprise size and industry specifics, with three implementation horizons and differentiated approach. The recommendations can be used for e-commerce enterprises' digital transformation strategies and state support programs. The proposed adaptation mechanisms optimize AI implementation and enhance Ukrainian enterprises' competitiveness. Further research will focus on industry-specific AI implementation strategies and their economic efficiency evaluation in European integration context.

Implementation of AI in the Medical Industry in the U.S.

The current literature reveals a gap in understanding how rehabilitation professionals, such as physical and occupational therapists, perceive and prepare to implement artificial intelligence (AI) in their practices. Therefore, we conducted a cross-sectional observational study to assess the perceptions, knowledge, and willingness of rehabilitation healthcare providers to implement AI in practice. This study was conducted in Saudi Arabia, with data collected from 430 physical therapy professionals via an online SurveyMonkey questionnaire between January and March 2024. The survey assessed demographics, AI knowledge and skills, and perceived challenges. Data were analyzed using Statistical Package for the Social Science (SPSS 27) and DATAtab (version 2025), with frequencies, percentages, and nonparametric tests used to examine the relationships between the variables. The majority of respondents (80.9%) believed that AI would be integrated into physical therapy in future, with 78.6% seeing AI as significantly impacting their work. While 61.4% thought that AI would reduce workload and enhance productivity, only 30% expressed concerns about AI endangering their profession. A lack of formal AI training has commonly been reported, with social media platforms being respondents' primary source of AI knowledge. Despite these challenges, 85.1% expressed an eagerness to learn and use AI. Organizational preparedness was a significant barrier, with 45.6% of respondents reporting that their organizations lacked AI strategies. There were insignificant differences in the mean rank of AI perceptions or knowledge based on the gender, years of experience, and qualification degree of the respondents. The results demonstrated a strong interest in AI implementation in physical therapy. The majority of respondents expressed confidence in AI's future utility and readiness to incorporate it into their practice. However, challenges, such as a lack of formal training and organizational preparedness, were identified. Overall, the findings highlight AI's potential to revolutionize physical therapy while underscoring the necessity to address training and readiness to fully realize this potential.

The implementation of Artificial Intelligence (AI) in managing single identification number systems, such as Indonesia's National Identity Number, must balance constitutional principles and human rights. AI can enhance administrative efficiency and data security, but it must align with the principle of equality before the law, as stipulated in Articles 27 and 29 of the 1945 Constitution. Ensuring data privacy and preventing discrimination in data processing are essential to avoid regional and social inequality. This study aims to examine how AI-based solutions impact the principles of equality before the law and personal data protection, as well as analyse these issues from the perspective of positivist philosophy, which emphasizes strict legal compliance and effective supervision. Using a normative juridical method with statutory and analytical approaches, this research evaluates existing regulations and explores the interplay between legal structure and AI governance. The findings reveal that while AI can promote efficiency and consistency, its algorithms may also introduce bias, risking unfair outcomes in identification services. A robust regulatory framework and continuous oversight are necessary to address potential inequalities and misuse. From a positivist view, strict rule enforcement and transparent governance are essential to balance technological innovation with the protection of individual rights. [Conclusion] In conclusion, the integration of AI into single identification systems must comply with constitutional and legal principles to uphold equality before the law and protect personal data. Effective legal oversight and accountability are essential to maintain fairness, prevent discrimination, and ensure that AI systems benefit all citizens equitably.

This study attempted to present discussions and directions regarding artificial intelligence(AI) and the prevention of corruption in Korean real estate. This Paper dealt with moral hazard, corruption prevention, and trust using real estate, the 4th industrial revolution(4IR), AI, and intelligent information technology as keywords. Prior research is focusing on economic utility and technical benefits related to AI and real estate. In contrast, this study pointed out the risk of moral hazard and corruption arising from the combination of AI and real estate, and conducted a paper to prevent moral hazard and corruption. In this respect, there is a difference between this study and existing previous studies. This is where the originality of this study appears. The results of this study on anti-corruption of AI and Korean real estate are as follows. As a result of the study, the problems of AI and Korean real estate, and anti-corruption were presented in three major categories: 1. AI and the problem of changing times, 2. Korean real estate and corruption problem, and 3. AI and real estate problem. In relation to this, this study largely derived three measures as implications of the research. The contents include, first, responses to the issues of AI and changing times, second, responses to Korean real estate and corruption issues, and third, measures to respond to AI and real estate issues. This study hopes to restore market trust through Artificial Intelligence(AI) and the prevention of corruption in Korean real estate, and to build a future where the benefits and utility provided by real estate, AI, and intelligent information technology are desirable.