설명가능한 인공지능(XAI) 기술도입이 금융서비스의 혁신성과를 향상시키는가?

This article is devoted to the creation of a model of observational diagnostics for patients in dentistry using artificial intelligence as an information and measurement system. Artificial intelligence technology and algorithms in the field of clinical and radiographic image analysis in general dentistry and orthopedics have become the most common systems, contributing to accurate and timely diagnosis of a particular patient. Along with clinical practice images, radiographs are key tools for graphic visualization of the patient's dentition. A model of observational diagnostics for patients using artificial intelligence technology as the latest tool in the healthcare industry can become a structured method of information standardization and measurements in general dentistry. The model of observational diagnostics can serve as a fundamental component of functional diagnostics based on artificial intelligence algorithms, since they have a special potential for increasing the reliability and consistency of diagnostic measurements and information used by a dentist during treatment planning and restoration of the dentition. The model of observational diagnostics of dental patients presented by the authors implies a significant step forward in the development of highprecision methods of functional diagnostics, monitoring and analysis of patient condition measurements. The algorithm of the observational diagnostics model was integrated into the program, the operation of which is based on artificial intelligence technology in order to increase the efficiency, consistency, optimality and accuracy of the processes carried out in clinical practice. The purpose of the study is to determine the characteristics of the model of observational diagnostics of dental patients using artificial intelligence as an information and measuring system. Main results of the study: 1) the characteristics of the practical application of artificial intelligence technology for radiographs, clinical images of patients' teeth are provided; 2) the essential aspects of the model of observational diagnostics of dental patients, as well as the features of its practical application for planning dental treatment of patients are highlighted; 3) the results of practical testing of the model of observational diagnostics of dental patients are reflected (using the example of detection and treatment of caries). Research materials: the results of academic studies on the assessment of functional diagnostics of oral cavity and dentition pathologies using artificial intelligence technology; practical testing of the model of observational diagnostics for dental patients (using the example of detection and treatment of caries in a 49yearold woman). Research methods: systematization, comparative analysis, contrastive analysis, generalization, abstractlogical method, induction, deduction, systems approach, data conceptualization, graphical and tabular visualization of data, construction of informationmeasuring systems, artificial intelligence methods, study of clinical images and radiographs of patients' dental arches. The main conclusions of the study: 1) information and measuring instruments, the operation of which is based on artificial intelligence technology, have become the basis for the rapid processing of significant amounts of information, increasing the accuracy and efficiency of clinical decisions in dentistry. These information and measuring systems allow us to determine complex patterns on radiographs and clinical images of dental arches, to increase the degree of accuracy of functional diagnostics, in particular, in the presence of subtle pathologies and disorders; 2) artificial intelligence technology cannot replace a qualified dentist, such information and measuring systems only complement the accumulated human professional experience, allowing us to jointly carry out functional diagnostics of patients with a dental profile; 3) the potential of artificial intelligence technologies to create comprehensive plans and reports for dental treatment and dental restoration forms the basis for the joint activities of the latest tools and a dentist. In the context of the study, it is noted that artificial intelligence algorithms have become a powerful informationmeasuring tool for improving the decisionmaking process in dental practice, since digital technology promptly and quickly provides feedback to the dentist about missed anomalies or suboptimal methods of dental treatment and restorative medicine. Integration of the model of observational diagnostics for dental patients will make it possible to form an algorithm for the operation of artificial intelligence that fully complies with the recommendations for functional diagnostics and dental treatment of patients, developed on the basis of factual information. The use of artificial intelligence algorithms in dental practice will increase the efficiency of the general industry standard for providing dental services to patients.

Artificial intelligence (AI) technology has shown tremendous contributions in various applications like speech recognition, expert systems, computer vision, robotics, and gaming. machine learning (ML) and deep learning (DL) algorithms under AI address problems such as prediction, classification, and regression. AI has touched many domains. The results or the predictions generated by these algorithms are not easily accepted by the user. Especially, the Healthcare domain is facing a great challenge in accepting the results or the predictions with the concern, Are AI results reliable, correct, and ethical? Doctors or medical practitioners are not ready to treat patients based on results or suggestions generated by AI algorithms. Hence, a technology that can explain how the results returned by AI algorithms are trustworthy, transparent, and interpretable was strongly needed. This need has given rise to the latest technology-explainable artificial intelligence (XAI). With the use of XAI, all the predictions, classifications made by AI algorithms are explainable, auditable, comprehensive, validating, and socially acceptable. This paper discusses explaining the results of breast cancer prediction as a case study. The results show that such an explanation will build trust in the doctors and hence will increase the acceptance of the AI-based systems.

Problem setting. Artificial intelligence is rapidly affecting the financial sector with countless potential benefits in terms of improving financial services and compliance. In the financial sector, artificial intelligence algorithms are already trusted to account for transactions, detect fraudulent schemes, assess customer creditworthiness, resource planning and reporting. But the introduction of such technologies entails new risks. Analysis of resent researches and publications. The following scientists were engaged in research of the specified question: D.W. Arner, J. Barberis, R.P. Buckley, Jon Truby, Rafael Brown, Andrew Dahdal, O. A. Baranov, O. V. Vinnyk, I. V. Yakovyuk, A. P. Voloshin, A. O. Shovkun, N.B. Patsuriia. Target of research. The aim of the article is to identify key strategic issues in developing mechanisms to ensure the effective implementation and use of artificial intelligence in the financial services market. Article’s main body. The paper investigates an important scientific and practical problem of legal regulation of artificial intelligence used by financial services market participants. The legal risks associated with the use of artificial intelligence programs in a particular area are considered. The most pressing risks to address targeted AI regulation are fundamental rights, data confidentiality, security and effective performance, and accountability. This article argues that the best way to encourage a sustainable future in AI innovation in the financial sector is to support a proactive regulatory approach prior to any financial harm occurring. This article argues that it would be optimal for policymakers to intervene early with targeted, proactive but balanced regulatory approaches to AI technology in the financial sector that are consistent with emerging internationally accepted principles on AI governance. Conclusions and prospects for the development. The adoption of rational regulations that encourage innovation whilst ensuring adherence to international principles will significantly reduce the likelihood that AI-related risks will develop into systemic problems. Leaving the financial sector only with voluntary codes of practice may encourage experimentation that in turn may result in innovative benefits – but it will definitely render customers vulnerable, institutions exposed and the entire financial system weakened.

The article explores the issue of the possible use of artificial intelligence (AI) algorithms in the field of game development. The increasing complexity of video games (game mechanics, diverse plots, game environments) necessitates the effective use of the latest AI technologies for game development. The aim of the work is to investigate the possibility of using algorithms in game development and gameplay. The hypothesis of the study is that the use of AI in game development (GameDev) can lead to a reduction in development costs. AI can be used to generate game environment landscape textures that adapt to the actions of real characters. Dialogues of background non-player characters (NPCs) and sound effects can be generated using AI. AI can generate NPCs whose behavior can change depending on the actions of real players. This creates an exciting gaming experience for players. This approach can reduce the cost and duration of production and positively affect the diversity of the gameplay. The article reviews and systematizes AI algorithms that can be used in game development, provides examples, and discusses the specifics and advantages of their use. For this purpose, a research methodology was used that combined the analysis of literary sources and conducting research based on information about the implementation of AI in GameDev. AI algorithms have great potential for effective implementation in GameDev, because their implementation helps to diversify the gameplay, provides a unique gaming experience for each player. The player receives significant freedom of action, various possible dynamically changing scenarios and dialogues with NPCs. AI can not only respond to the player's actions, but also change the environment to make the gameplay interesting and exciting for the player, regardless of the level of his progress in the game. The implementation of AI technologies in GameDev mostly shows good efficiency, but they require high qualifications of developers and consideration of risks in the process of its implementation.

With the rapid advancement of artificial intelligence (AI) technology, its application in the financial sector is becoming increasingly widespread, playing a more significant role, particularly in the stock market. Statistics show that global investment in AI technology within the fintech market grew from approximately $8 billion in 2020 to nearly $20 billion in 2023, with a compound annual growth rate (CAGR) exceeding 30%. This paper systematically examines the current application status of AI technology in the Chinese stock market, its effects on market efficiency, changes in investor behavior, macroeconomic impacts, and relevant policy recommendations. Utilizing methods such as literature review, empirical analysis, case studies, and model construction, this study finds that AI holds significant advantages in data processing, trade execution, and market prediction, but also faces challenges such as technical adaptability and risk management. The results indicate that AI technology can enhance market efficiency, alter investor behavior, and exert profound macroeconomic influences. Finally, the paper proposes corresponding policy recommendations and future research directions to promote the healthy application and development of AI in the Chinese stock market.

Artificial intelligence (AI) has profoundly altered the financial services industry by offering new paradigms for data analysis, decision-making, and operational efficiency. This chapter provides an extensive review of the core concepts, recent advancements, and emerging applications of AI in the financial sector. Based on a critical review of 162 high-impact scientific articles, the chapter identifies and evaluates key theme areas, including financial forecasting, fraud detection, explainable AI (XAI), and the nexus between AI and FinTech. The findings demonstrate the importance of state-of-the-art AI techniques such as deep learning, reinforcement learning, and hybrid models for enhancing risk assessment, raising forecast precision, and ensuring regulatory compliance. The chapter also discusses how AI could transform several financial processes, including portfolio management, credit assessment, and anti-money laundering efforts. The creation of interpretable AI frameworks, the incorporation of multimodal data sources, and the examination of AI's societal ramifications regarding sustainability and financial inclusion are just a few of the new opportunities and research gaps that are highlighted in this chapter. By bridging the gap between academic research and industry practice, this chapter provides useful insights for both researchers and practitioners. To harness AI's transformative potential while upholding moral standards and public trust, it encourages ethical, interdisciplinary collaboration. The chapter concludes with a strategic roadmap for the innovative and long-term use of AI technology in contemporary financial ecosystems.

Artificial intelligence (AI) technologies have revolutionized numerous sectors, enhancing efficiency, innovation, and convenience. However, AI's rise has highlighted a critical concern: bias within AI algorithms. This study uses a systematic literature review and analysis of real-world case studies to explore the forms, underlying causes, and methods for detecting and mitigating bias in AI. We identify key sources of bias, such as skewed training data and societal influences, and analyze their impact on marginalized communities. Our findings reveal that algorithmic transparency and fairnessaware learning are among the most effective strategies for reducing bias. Additionally, we address the challenges of regulatory frameworks and ethical considerations, advocating for robust accountability mechanisms and ethical development practices. By highlighting future research directions and encouraging collective efforts toward fairness and equity, this study underscores the importance of addressing bias in AI algorithms and upholding ethical standards in AI technologies.

Integrating artificial intelligence and machine learning technology with the financial sector has resulted in the emergence of smart finance. This chapter summarizes the complex interconnection between artificial intelligence, machine learning, and the dynamic environment of financial systems. This chapter examines the expanding use of algorithms in financial decision-making and analyzes how artificial intelligence and machine learning may be applied. These applications include improving algorithmic trading techniques, enhancing the accuracy of credit scoring, optimizing risk assessment, and providing personalized customer care. Nevertheless, incorporating technology has challenges, encompassing ethical deliberations and regulatory obstacles. The chapter also explores future developments, including topics such as explainable AI, integration of blockchain technology, and the possible influence of quantum computing on financial paradigms. This research examines the complex relationship between artificial intelligence, machine learning, and the future direction of smart finance.

In recent years, artificial intelligence technology is deepening and expanding, and many industries begin to use artificial intelligence technology to improve efficiency and accuracy. Artificial intelligence technology is more and more widely used in the field of sports. Machine vision, infrared technology, electronic induction technology and computer control technology in artificial intelligence can play a role in all aspects of the field of sports. Unlike other computer technologies, artificial intelligence can imitate the human brain for decision-making. Neural networks in artificial intelligence technology can reason based on Hebb rules and a large number of factual data. Moreover, the genetic algorithm in artificial intelligence can be continuously improved based on the coding of individuals in the population and the determination of fitness function, and the decision-making can be more and more accurate. The ability of artificial intelligence to make decisions can bring a new situation to sports competition. The fairness of sports competition will be improved because of artificial intelligence technology, and each team can make decisions according to the strategy analyzed by artificial intelligence technology.

This study investigates the integration of Artificial Intelligence (AI) as a co-pilot in aviation. As aviation increasingly embraces automation, concerns about safety, pilot roles, and ethical implications grow. The research emphasizes the necessity for human-AI collaboration in which AI enhances pilot capabilities without replacing them. Using a mixed-methods approach that includes literature review, case analysis, and stakeholder input, the study provides a model for safely integrating AI into flight operations. Key findings support that AI should assist rather than replace pilots, maintaining human oversight in high-stakes environments. The thesis concludes with policy and design recommendations for AI integration in aviation. This comprehensive study explores the transformative potential of Artificial Intelligence (AI) in modern commercial aviation, specifically emphasizing the concept of AI as a co-pilot. With rising operational complexity, global pilot shortages, and increasing demand for enhanced safety and efficiency, AI presents itself as a promising technological innovation. It has the capacity to provide real-time flight data interpretation, system anomaly alerts, autonomous recommendations, and task automation that collectively enhance situational awareness and reduce pilot workload. However, the expansion of AI usage in aviation also introduces critical concerns around pilot-AI interaction, explainability of AI systems, cybersecurity risks, and ethical responsibility in cases of failure. The research argues for a balanced integration model where AI supports—rather than supplants—human pilots. The idea of a "co-pilot AI" is conceptualized to highlight systems that work in tandem with human decision-makers, offering real-time insights while respecting human authority. Human-in-the-loop (HITL) design frameworks, explainable AI (XAI) principles, and trust-calibration models were central to the theoretical foundation of this research. A mixed-methods approach was adopted to strengthen the analysis, combining literature reviews, international aviation policy reviews, expert interviews, and comparative incident analysis. The findings of the study reveal a general consensus among professionals that while AI significantly enhances operational responsiveness and data processing capabilities, human oversight remains non-negotiable for moral judgment and adaptive decision-making during complex flight conditions. The research further identifies that system transparency, pilot familiarity, and regulatory clarity are essential for cultivating pilot trust in AI tools. It also highlights training gaps in existing aviation programs, where pilots are inadequately prepared to interface effectively with AI technologies. From a managerial and policy standpoint, the study recommends a strategic shift in training curricula to include AI-system literacy, greater investment in explainable interfaces, cross-industry collaboration for best practices, and harmonization of international regulatory standards. These measures, if implemented, will not only prevent technological overdependence but also build a more resilient and adaptive aviation ecosystem.

Professionals are implementing artificial intelligence (AI) technology in different fields owing to its diverse uses and benefits. Similarly, AI professionals are also beginning to implement AI technology in foreign-language education and second-language learning. Therefore, through a systematic literature review, this paper analyzes the role of AI in helping learners of a second language to master pronunciation. A detailed and in-depth search of different well-known databases was conducted, and of 116 articles, only 39 were selected for this paper. AI algorithms can advance language learning and acquisition in almost every dialect and could be significant for different parties in different ways. For example, organizations could utilize AI technology to develop their workers’ knowledge; individual learners could use AI technology to facilitate their studies anywhere and anytime; and traditional learning institutions could incorporate AI-powered methods of language learning to diversify learners’ opportunities. There are many benefits to employing AI in language learning, particularly in second-language learning.

As more and more financial services make use of artificial intelligence (AI), AI decisions are starting to impact matters that people care about—money. Across many areas, AI is now being trusted to meet regulatory compliance for high-quality training data, create user-friendly transparent requirements, and manage risk. As regulators take the time to better understand how to regulate AI technologies in tandem with engaging with AI innovators from the financial sector, new risks must be considered that AI technologies could bring to the financial services and what actions could be taken to mitigate them. AI systems have found a wide range of application areas in financial services but their involvement in high-stakes decisions has escalated the need for compliance and effective model governance. The unique characteristics of AI, along with the governance practices that followed, have led to fundamental tensions that lie at the heart of challenges in AI model governance. Furthermore, the growth in model complexity raises questions on the sustainability of practices that are already under strain. Governance with regard to AI is still very much in its infancy, with regulators gradually catching up to the innovative development in AI. Banking sector struggles with issues such as deciding if tailor-made models to customer needs and newly created products are fundamentally sound and have submitted prevention measures to control their risks and the maintenance of model-derived decisions after deployment. The governance of complex AI systems lends itself to a systemic view that considers the multiplication of models, sources, systems, teams across locations and regulators. It is key to also embed tackling mixed-nationality jurisdiction challenges into future regulations of algorithms in the banking sector as it is one of the key current matters discussed by authorities [2]. It would be best to take all socio-political pressures immediately into account to develop a comprehensive AI regulation framework for the deployment stage that takes into consideration and finds a balance across its risks while encouraging its deployment to spur the efficiency, cost and quality of products and services, risk-averse behaviours of customers and therefore the stability of the banking sector as a whole.

The article is devoted to the study of the application of artificial intelligence technologies in law enforcement activities in Ukraine and the world. The purpose of the article is to analyze the international and domestic experience of using digital technologies in law enforcement activities, to identify the shortcomings of the legal regulation of the use of artificial intelligence in this area, to provide proposals for its improvement in Ukraine. It was found that one of the countries that is actively expanding the use of artificial intelligence and successfully introducing relevant technologies into public administration is China. The artificial intelligence system which is used in China called «Zero Trust». The purpose of using this system is to prevent the commission of corruption offenses. It is emphasized, that the disadvantage of the «Zero Trust» system is that artificial intelligence, identifying the cause of corruption, does not indicate the algorithms and principles of this process. At the same time, the experience of using artificial intelligence in the field of preventing and combating corruption in Ukraine is considered when carrying out automated verification of persons declarations of authorized to perform state and local government functions. Ways of improving legal regulation when conducting financial control of declarations using artificial intelligence technologies are proposed. It has been found that artificial intelligence is widely used in the USA to predict the prevalence and state of crime. It was emphasized that Ukraine also has experience in the application of artificial intelligence technologies in law enforcement activities. Artificial intelligence is used in the development and implementation of departmental specialized intelligent software, the implementation of operative and investigative actions (for the recognition of video objects observation by comparing images in social networks, etc). It was concluded that artificial intelligence and digital technologies help to quickly process large volumes of information in the investigation and prevention of criminal activity. Modern developments in the field of artificial intelligence and advanced digital technologies, which are implemented in foreign countries to prevent and fight crime, are of great importance for the improvement of legal regulation in this field in Ukraine.

Background The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow. Objective The aim of this study is to test the performance of and physicians’ preferences regarding an AI algorithm during the evaluation of patients via teledermatology. Methods We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician’s diagnosis. Physicians also reported whether the AI algorithm was useful or not. Results The balanced (in-distribution) top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P=.049). Exposure to the AI algorithm results was considered useful in 11.8% of visits (n=40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n=2) of cases. Algorithm performance was associated with patient skin type and image quality. Conclusions AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine. Conflicts of Interest None declared.

The financial sector has experienced significant transformation with the adoption of Artificial Intelligence (AI) technology, particularly in improving business decision-making. This study aims to analyze the impact of AI on decision-making quality, focusing on risk analysis and portfolio management in Indonesia's financial sector. A mixed-method approach was utilized, combining quantitative and qualitative data to provide a comprehensive view of AI’s role in financial decision-making processes. Quantitative data were gathered through surveys of 50 respondents from various financial institutions, while qualitative data were obtained from semi-structured interviews with industry executives. The findings indicate that AI significantly enhances risk analysis accuracy by 25%, optimizes portfolio management, accelerates decision-making processes, and improves operational efficiency by automating manual tasks and reducing human errors. Despite these benefits, the study also identifies challenges such as data quality issues and high implementation costs, which hinder the broader adoption of AI in the financial sector. The study concludes that AI offers substantial potential to improve decision-making in the financial industry, but addressing data infrastructure and training needs is critical for achieving optimal outcomes