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1
Pashkova, Nataliya V., and Maxim V. Vesnyanov. "Ethical issues in using artificial intelligence technologies." Alma mater. Vestnik Vysshey Shkoly, no. 1 (January 2024): 107–10. http://dx.doi.org/10.20339/am.01-24.107.
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The article discusses the ethical aspects associated with the use and development of artificial intelligence technologies. With the development of AI, problems arise that affect various areas of human life, including ethics, economics, medicine, education and others. Analyzes key ethical issues such as the autonomy of AI decision-making; determining who is responsible for damage caused by AI; transparency and data protection, as well as issues of inequality and the potential social consequences of AI. Our task was to identify the main ethical issues related to the use of artificial intelligence and to identify possible solutions for the use and development of AI. To address this, we reviewed and analyzed existing ethical and guiding principles, identified and systematized key ethical issues and different positions on AI. The theoretical sources for our analysis were the results of scientific research in the field of artificial intelligence, considering the ethical aspects of the use and development of such systems.
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Sarwari, Abdul Qahar, and Hamedi Mohd Adnan. "The effectiveness of artificial intelligence (AI) on daily educational activities of undergraduates in a modern and diversified university environment." Advances in Mobile Learning Educational Research 4, no. 1 (February 27, 2024): 927–30. http://dx.doi.org/10.25082/amler.2024.01.004.
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This study assessed the effectiveness of AI and AI-related technologies in the daily educational activities of undergraduate students in a modern and diversified university environment. The participants were 13 undergraduate Indonesian students participating in a mobility program for two weeks in Malaysia. A survey questionnaire designed with the help of the existing literature and ChatGPT, which includes ten (10) structured items and seven (7) open-ended questions, was used to collect the data. The relevant SPSS tests were used to analyze the data. Based on the results, of all 13 participants, 12 (92.3%) of them already experienced AI in their daily educational activities, and there were strong positive correlations between the attitudes toward AI and AI experiences, and attitudes toward AI and the effects of AI on education attributes, with correlation scores of .663 and .833 respectively. Based on the participant's answers to the qualitative questions, most of them believed that AI and AI technologies, such as ChatGPT, are helpful in daily educational activities and help them gain information regardless of time and space limitations and do their university-related assignments quickly. Based on the results, AI and AI-related technologies could transform different aspects of modern education.
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Kabashkin, Igor, Boriss Misnevs, and Olga Zervina. "Artificial Intelligence in Aviation: New Professionals for New Technologies." Applied Sciences 13, no. 21 (October 25, 2023): 11660. http://dx.doi.org/10.3390/app132111660.
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Major aviation organizations have highlighted the need to adopt artificial intelligence (AI) to transform operations and improve efficiency and safety. However, the aviation industry requires qualified graduates with relevant AI competencies to meet this demand. This study analyzed aviation engineering bachelor’s programs at European universities to determine if they are preparing students for AI integration in aviation by incorporating AI-related topics. The analysis focused on program descriptions and syllabi using semantic annotation. The results showed a limited focus on AI and machine learning competencies, with more emphasis on foundational digital skills. Reasons include the newness of aviation AI, its specialized nature, and implementation challenges. As the industry evolves, dedicated AI programs may emerge. But currently, curricula appear misaligned with stated industry goals for AI adoption. The study provides an analytical methodology and competency framework to help educators address this gap. Producing graduates equipped with AI literacy and collaboration skills will be key to aviation’s intelligent future.
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Cetinic, Eva, and James She. "Understanding and Creating Art with AI: Review and Outlook." ACM Transactions on Multimedia Computing, Communications, and Applications 18, no. 2 (May 31, 2022): 1–22. http://dx.doi.org/10.1145/3475799.
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Technologies related to artificial intelligence (AI) have a strong impact on the changes of research and creative practices in visual arts. The growing number of research initiatives and creative applications that emerge in the intersection of AI and art motivates us to examine and discuss the creative and explorative potentials of AI technologies in the context of art. This article provides an integrated review of two facets of AI and art: (1) AI is used for art analysis and employed on digitized artwork collections, or (2) AI is used for creative purposes and generating novel artworks. In the context of AI-related research for art understanding, we present a comprehensive overview of artwork datasets and recent works that address a variety of tasks such as classification, object detection, similarity retrieval, multimodal representations, and computational aesthetics, among others. In relation to the role of AI in creating art, we address various practical and theoretical aspects of AI Art and consolidate related works that deal with those topics in detail. Finally, we provide a concise outlook on the future progression and potential impact of AI technologies on our understanding and creation of art.
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Chen, Yinong, and Gennaro De Luca. "Technologies Supporting Artificial Intelligence and Robotics Application Development." Journal of Artificial Intelligence and Technology 1, no. 1 (January 31, 2021): 1–8. http://dx.doi.org/10.37965/jait.2020.0065.
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Artificial intelligence (AI) and robotics have gone through three generations of development, from Turing test, logic theory machine, to expert system and self-driving car. In the third-generation today, AI and robotics have collaboratively been used in many areas in our society, including industry, business, manufacture, research, and education. There are many challenging problems in developing AI and robotics applications. We launch this new Journal of Artificial Intelligence and Technology to facilitate the exchange of the latest research and practice in AI and technologies. In this inaugural issue, we first introduce a few key technologies and platforms supporting the third-generation AI and robotics application development based on stacks of technologies and platforms. We present examples of such development environments created by both industry and academia. We also selected eight papers in the related areas to celebrate the foundation of this journal.
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Qian, Zuo Yin, and Wei Sieng Lai. "THE IMPACT OF ARTIFICIAL INTELLIGENCE TECHNOLOGY ON INTERNATIONAL TRADE." Advanced International Journal of Business, Entrepreneurship and SMEs 6, no. 19 (March 12, 2024): 153–70. http://dx.doi.org/10.35631/aijbes.619012.
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This paper studies the complex impact mechanism of AI (artificial intelligence) technology on international trade. This provides an answer to how different countries use AI technology to increase their own international trade volume. This article is based on an analysis of cross-sectional data for 139 countries in 2021. This paper uses the Ordinary Least Square (OLS) method to perform a analysis on the cross-sectional data. This paper finds that the membership of World Trade Organization (WTO) has obvious significance for a country to use AI technology to promote exports. In countries with a high Government AI Readiness Index, AI technology has a significant role in promoting the growth of international trade. However, there is a significant negative correlation between the number of patent applications for AI-related technologies and imports and exports across countries. Besides, the number of patent applications for AI-related technologies has a direct heterogeneous impact on the imports of countries with different income groups. AI technology is an emerging technology. To study the impact of this technology on international trade, multiple factors of AI technology must be considered at the same time. For countries that are not members of the WTO, joining the WTO can make better use of AI technology to promote the development of their international trade. Between the complex relationship between the number of patent applications for different AI-related technologies and international trade, countries with different income groups should develop AI technologies that are beneficial to their own international trade.
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Rozenes, Shai, and Yuval Cohen. "Artificial Intelligence Synergetic Opportunities in Services: Conversational Systems Perspective." Applied Sciences 12, no. 16 (August 21, 2022): 8363. http://dx.doi.org/10.3390/app12168363.
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The importance of this paper is its discovery of the unused synergetic potential of integration between several AI techniques into an orchestrated effort to improve service. Special emphasis is given to the conversational capabilities of AI systems. The paper shows that the literature related to the use of AI in service is divided into independent knowledge domains (silos) that are either related to the technology under consideration, or to a small group of technologies related to a certain application; it then discusses the reasons for the isolation of these silos, and reveals the barriers and the traps for their integration. Two case studies of service systems are presented to illustrate the importance of synergy. A special focus is given to the conversation part of these service systems: the first case presents an application with high potential for integrating new AI technologies into its AI portfolio, while the second case illustrates the advantages of a mature application that has already integrated many technologies into its AI portfolio. Finally, the paper discusses the two case studies and presents inclusion relationships between AI capabilities to facilitate generating a roadmap for extending AI capabilities with synergetic opportunities.
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Okladnikova, S. V., and A. S. Pankrashov. "Application of artificial intelligence technologies in HR management." Herald of Dagestan State Technical University. Technical Sciences 50, no. 2 (July 31, 2023): 117–25. http://dx.doi.org/10.21822/2073-6185-2023-50-2-117-125.
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Objective. The purpose of the study is to analyze the possibility of using AI technologies to solve problems related to the selection of employees in project teams, based on production indicators and HR metrics of personnel.Method. Based on the fact that in the field of AI, methods mean algorithms by which tasks are solved, the following number of methods related to AI theory were identified: neural networks, fuzzy logic, expert systems, evolutionary modeling, Machine Learning.Result. An example of the use of AI is given in a situation where it is necessary to recruit personnel for a project based on the length of service and the degree of workload (where a scale with values from “highly loaded” to “not loaded” is used for workload). For the described example, an explanation is given that reveals the use of AI technologies (such as question-and-answer systems) in order to form HR metrics and production indicators. Additionally, the process of applying the objective function to obtain a numerical coefficient based on individual metrics or a combination of them is described in order to make a decision corresponding to its value based on the resulting indicator of the function.Conclusion. Within the framework of the conducted research, the historical patterns that led the field of personnel management to transformation and which made the use of AI technologies relevant in this area were considered. Continuous development and implementation of intelligent tools in the practice of project management facilitates HR processes and increases the efficiency of employee management. The use of AI technologies considered in the study will help to successfully monitor the state of both the project and the project team, which will have a positive impact on the productivity and profit of the enterprise.
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Grove, Hugh, Mac Clouse, and Tracy Xu. "New risks related to emerging technologies and reputation for corporate governance." Journal of Governance and Regulation 9, no. 2 (2020): 64–74. http://dx.doi.org/10.22495/jgrv9i2art4.
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Artificial intelligence (AI) has moved from theory into the global marketplace. The United Nations World Intellectual Property Organization released the first report of its Technology Trends series on January 31, 2019. It considered more than 340,000 AI-related patent applications over the last 70 years. 50 percent of all AI patents have been published in just the last five years. The challenges, potential risks, and opportunities for business and corporate governance from emerging technologies, especially artificial intelligence, have been summarized as whereby machines and software can analyze, optimize, prophesize, customize, digitize and automate just about any job in every industry. Boards of directors and executives need to recognize and understand the new risks associated with these emerging technologies and related reputational risks. The major research question of this paper is how boards of directors and executives can deal with both risk challenges and opportunities to strengthen corporate governance. Accordingly, the following sections of this paper discuss key risk management issues: deep shift risks, global risks, digital risks and opportunities, AI initiatives risks, business risks from millennials, business reputational risks, and conclusions.
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El-Mahrouki, Mayada Moustafa. "LEGISLATIVE INDUSTRY CHALLENGES IN CONFRONTING ARTIFICIAL INTELLIGENCE CRIMES." Journal of Law and Sustainable Development 12, no. 4 (April 2, 2024): e3566. http://dx.doi.org/10.55908/sdgs.v12i4.3566.
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Objectives: The objective of this scientific paper is to shed light on the challenges facing the legislative industry in the field of artificial intelligence (AI) technologies. Specifically, it aims to anticipate and predict the difficulties related to creating an independent legal personality for AI tools, enabling them to assume rights and obligations, as well as establishing criminal responsibility and punishment in cases where AI technologies commit crimes punishable by law.
Methods: To achieve the stated objectives, this paper employs a theoretical and analytical approach. It involves a comprehensive review and analysis of existing literature, legal frameworks, and case studies related to the legal implications of AI technologies. Additionally, hypothetical scenarios and potential challenges are discussed to illustrate the complexities involved in creating legal frameworks that address the unique nature of AI tools and their impact on society.
Results: The analysis reveals several key challenges facing the legislative industry in the field of AI technologies. These challenges include the conceptualization of an independent legal personality for AI tools, determining the rights and obligations associated with such personality, and establishing mechanisms for holding AI technologies accountable for their actions. Furthermore, the paper explores the difficulties in imposing criminal responsibility and punishment on AI technologies in cases where they commit crimes.
Conclusion: In conclusion, this scientific paper highlights the complexities and challenges associated with creating legal frameworks for AI technologies. By anticipating and predicting these challenges, it provides valuable insights for legislators, policymakers, and legal professionals involved in shaping the regulatory landscape for AI. Moving forward, it is essential to address these challenges proactively and develop legal frameworks that strike a balance between promoting innovation and safeguarding societal interests in the era of artificial intelligence.
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MUNIRATHINAM, DR R. BABY, A.TEJASWINI, M.SAMREEN, and T.PRAVANYA. "ARTIFICIAL INTELLIGENCE CRIME' AN OVERVIEW OF MALICIOUS USE AND ABUSE OF AI." International Journal of Engineering, Science and Advanced Technology 24, no. 10 (2024): 25–31. http://dx.doi.org/10.36893/ijesat.2024.v24i10.04.
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The rapid advancement of artificial intelligence (AI) technologies has brought about significant benefits across various sectors, yet it has also introduced new challenges and risks associated with their malicious use and abuse. This project provides a comprehensive overview of AI-related crimes, exploring the spectrum of nefarious activities that exploit AI systems for illegal and unethical purposes. It examines the ways in which AI can be weaponized for cyberattacks, including automated phishing, deepfake creation, and data breaches. Additionally, the project delves into the misuse of AI in surveillance and privacy violations, highlighting how these technologies can be employed to infringe upon individual freedoms and civil rights. Through a detailed analysis of case studies, regulatory gaps, and emerging threats, this study aims to shed light on the various forms of AI abuse and offer recommendations for mitigating associated risks. By addressing the dual-use nature of AI technologies, the project seeks to enhance awareness and contribute to the development of effective safeguards and ethical guidelines to prevent and address AI-related crimes.
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Song, Fei, Jun Sun, and Tao Wang. "Overview of Natural Language Processing Technologies and Rationales in Application." Theory and Practice in Language Studies 10, no. 1 (December 24, 2019): 49. http://dx.doi.org/10.17507/tpls.1001.07.
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In the past decade, rapid advancement of new technologies including data technology, virtual reality (VR) and artificial intelligence (AI), which are all related to language disciplines, brings a new era of data-based language studies, relying on AI to enhance the language ability and VI to create fresh new experience. Practice of language processing in language disciplines by those technologies in turn promotes the emergence of some other revolutionary technologies, for example, the increasingly common data thinking and computational thinking in language research. In this context, it is of great significance to seize the opportunity of big data era, and make full use of AI and other new technologies to substantially promote language-related studies. Thus, an overview of several important language processing technologies and the corresponding rationales, as well as the latest progress is expounded in this paper.
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Shevtsova, Daria, Anam Ahmed, Iris W. A. Boot, Carmen Sanges, Michael Hudecek, John J. L. Jacobs, Simon Hort, and Hubertus J. M. Vrijhoef. "Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study." JMIR Human Factors 11 (January 17, 2024): e47031. http://dx.doi.org/10.2196/47031.
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Background Artificial intelligence (AI)–powered technologies are being increasingly used in almost all fields, including medicine. However, to successfully implement medical AI applications, ensuring trust and acceptance toward such technologies is crucial for their successful spread and timely adoption worldwide. Although AI applications in medicine provide advantages to the current health care system, there are also various associated challenges regarding, for instance, data privacy, accountability, and equity and fairness, which could hinder medical AI application implementation. Objective The aim of this study was to identify factors related to trust in and acceptance of novel AI-powered medical technologies and to assess the relevance of those factors among relevant stakeholders. Methods This study used a mixed methods design. First, a rapid review of the existing literature was conducted, aiming to identify various factors related to trust in and acceptance of novel AI applications in medicine. Next, an electronic survey including the rapid review–derived factors was disseminated among key stakeholder groups. Participants (N=22) were asked to assess on a 5-point Likert scale (1=irrelevant to 5=relevant) to what extent they thought the various factors (N=19) were relevant to trust in and acceptance of novel AI applications in medicine. Results The rapid review (N=32 papers) yielded 110 factors related to trust and 77 factors related to acceptance toward AI technology in medicine. Closely related factors were assigned to 1 of the 19 overarching umbrella factors, which were further grouped into 4 categories: human-related (ie, the type of institution AI professionals originate from), technology-related (ie, the explainability and transparency of AI application processes and outcomes), ethical and legal (ie, data use transparency), and additional factors (ie, AI applications being environment friendly). The categorized 19 umbrella factors were presented as survey statements, which were evaluated by relevant stakeholders. Survey participants (N=22) represented researchers (n=18, 82%), technology providers (n=5, 23%), hospital staff (n=3, 14%), and policy makers (n=3, 14%). Of the 19 factors, 16 (84%) human-related, technology-related, ethical and legal, and additional factors were considered to be of high relevance to trust in and acceptance of novel AI applications in medicine. The patient’s gender, age, and education level were found to be of low relevance (3/19, 16%). Conclusions The results of this study could help the implementers of medical AI applications to understand what drives trust and acceptance toward AI-powered technologies among key stakeholders in medicine. Consequently, this would allow the implementers to identify strategies that facilitate trust in and acceptance of medical AI applications among key stakeholders and potential users.
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Yigitcanlar, Tan, Nayomi Kankanamge, Massimo Regona, Andres Ruiz Maldonado, Bridget Rowan, Alex Ryu, Kevin C. Desouza, Juan M. Corchado, Rashid Mehmood, and Rita Yi Man Li. "Artificial Intelligence Technologies and Related Urban Planning and Development Concepts: How Are They Perceived and Utilized in Australia?" Journal of Open Innovation: Technology, Market, and Complexity 6, no. 4 (December 11, 2020): 187. http://dx.doi.org/10.3390/joitmc6040187.
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Artificial intelligence (AI) is a powerful technology with an increasing popularity and applications in areas ranging from marketing to banking and finance, from agriculture to healthcare and security, from space exploration to robotics and transport, and from chatbots to artificial creativity and manufacturing. Although many of these areas closely relate to the urban context, there is limited understanding of the trending AI technologies and their application areas—or concepts—in the urban planning and development fields. Similarly, there is a knowledge gap in how the public perceives AI technologies, their application areas, and the AI-related policies and practices of our cities. This study aims to advance our understanding of the relationship between the key AI technologies (n = 15) and their key application areas (n = 16) in urban planning and development. To this end, this study examines public perceptions of how AI technologies and their application areas in urban planning and development are perceived and utilized in the testbed case study of Australian states and territories. The methodological approach of this study employs the social media analytics method, and conducts sentiment and content analyses of location-based Twitter messages (n = 11,236) from Australia. The results disclose that: (a) digital transformation, innovation, and sustainability are the most popular AI application areas in urban planning and development; (b) drones, automation, robotics, and big data are the most popular AI technologies utilized in urban planning and development, and; (c) achieving the digital transformation and sustainability of cities through the use of AI technologies—such as big data, automation and robotics—is the central community discussion topic.
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Jeong, Dae-hyun, O. Young Kown, and Eunjin Hwang. "Technological Convergence of AI Across the Industrial Sectors." International Journal on Advanced Science, Engineering and Information Technology 14, no. 4 (August 2, 2024): 1152–60. http://dx.doi.org/10.18517/ijaseit.14.4.18077.
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The AI market has been experiencing significant growth recently and is projected to thrive. Yet, there is still a lack of comprehensive studies integrating diverse industries and technologies in AI. Furthermore, AI-related patent analysis often examines AI technologies without considering their convergence with other sectors. Therefore, to fill this gap, this study aims to explore the technological convergence of AI using a network analysis approach with patent data in finance & management, healthcare, semiconductors, games, biotechnology, and transport. This study used an IPC-based convergence network methodology to define critical industrial areas and influential technologies with the four-digit IPC codes for the AI patent group from 2000 to 2019. Moreover, this study conducted a centrality analysis using Net-Miner software to identify hubs and connected nodes and analyze the comprehensive convergence status related to AI. According to the results, we defined hub nodes based on the degree and centralities and analyzed the centrality of six sectors in the AI convergence network. In addition, the technology classification of solid ties is analyzed based on IPC network analysis. Finally, this study attempts to deliver theoretical and empirical contributions to technological convergence, providing a comprehensive framework for understanding how different technologies can converge in AI with three categories: 1) learning and reasoning, 2) natural language processing, and 3) computer vision. This study suggests that companies operating within the industrial AI space should reflect the evolution of technology as revealed in the mainstream trends of sector-specific AI integration.
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Yee, Do-Hyung, and Yen-Yoo You. "The Impact of Awareness of New Artificial Intelligence Technologies on Policy Governance on Risk." Research in World Economy 11, no. 2 (May 23, 2020): 152. http://dx.doi.org/10.5430/rwe.v11n2p152.
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Background/Objectives: This study examined the risks of new AI technologies and their impact on policy governance. Artificial intelligence is bringing about changes in various fields such as politics, economy and culture through information society and technology. In particular, it has a positive effect on solving various problems of existing society and overcoming limitations. But this advancement in artificial intelligence can create the opposite problem as expected. This appears to be a risk. We identify the factors that recognize this risk and investigate the possible impact on government governance.Methods/Statistical analysis: The questionnaire and data of this journal were analyzed by Korean public portal data, and the analysis data were designated by the Korea Information Technology Agency, AI related company, AI association, Ministry of Science, ICT and Future Planning, IT society, government research institute, Korea Communications Commission, and National Security Agency The questionnaire survey was based on AI experts working in the field.The analysis program uses IBM SPSS Statistics 22. The analysis methods are descriptive statistical analysis, reliability analysis and exploratory factor analysis.Findings: This study examined the risks of new AI technologies and their impact on policy governance. The survey was conducted to clarify comments on awareness of new AI-related technologies, awareness of AI risks, and improvements to AI-related policies.AI risk has become an integral part of regulation and the government's role as a risk manager is important.Improvements/Applications: Further discussion is needed regarding the commercialization effects of AI technology awareness, benefit items and timing items on policy governance through risk awareness.
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Santos, Roberto S., and Lingling Qin. "Risk Capital and Emerging Technologies: Innovation and Investment Patterns Based on Artificial Intelligence Patent Data Analysis." Journal of Risk and Financial Management 12, no. 4 (December 14, 2019): 189. http://dx.doi.org/10.3390/jrfm12040189.
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The promise of artificial intelligence (AI) to drive economic growth and improve quality of life has ushered in a new AI arms race. Investments of risk capital fuel this emerging technology. We examine the role that venture capital (VC) and corporate investments of risk capital play in the emergence of AI-related technologies. Drawing upon a dataset of 29,954 U.S. patents from 1970 to 2018, including 1484 U.S. patents granted to 224 VC-backed start-ups, we identify AI-related innovation and investment characteristics. Furthermore, we develop a new measure of knowledge coupling at the firm-level and use this to explore how knowledge coupling influences VC risk capital decisions in emerging AI technologies. Our findings show that knowledge coupling is a better predictor of VC investment in emerging technologies than the breadth of a patent’s technological domains. Furthermore, our results show that there are differences in knowledge coupling between private start-ups and public corporations. These findings enhance our understanding of what types of AI innovations are more likely to be selected by VCs and have important implications for our understanding of how risk capital induces the emergence of new technologies.
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Fedorov, M. V. "Socio-economic aspects of the introduction of artificial intelligence technologies." Journal of Digital Economy Research 1, no. 1 (March 10, 2023): 6–60. http://dx.doi.org/10.24833/14511791-2023-1-6-60.
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The main objective of the paper is to give an overview of global effects of AI technologies, including socio-ethical principles, direct and non-direct economic impact and regulatory frameworks for developing strategies of sustainable development based on AI technologies. We will discuss these problems considering AI as a part of the global process of technological development, and, therefore, will briefly overview relationships between AI and other close fields (computational technologies, data acquisition techniques etc). A particular focus will be on global risks associated with the intensive use of AI technologies. Special attention will be given to the issues of international standardization of AI and related technologies. A section on AI-based social ranking will discuss fundamental problems inherent for such systems (biases, non-transparency etc). That section will be followed by a section on deepfakes which will be discussed in view of their dramatic effect on the conception of trust, both on individual and population/state levels. The paper will also discuss effects of widespread introduction of AI on other fields of research, such as chemical sciences and molecular biology. We will discuss pathways for sustainable development of “Trustworthy AI” which may achieve the desired balance between the benefits and risks of using these technologies and a global scale. We will discuss approaches that may lead to development of strategic principles for accessing long term effects of AI followed by relevant regulatory approaches.
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Kosasi, Sandy, Chandra Lukita, Mochamad Heru Riza Chakim, Adam Faturahman, and Dhiyah Ayu Rini Kusumawardhani. "The Influence of Digital Artificial Intelligence Technology on Quality of Life with a Global Perspective." Aptisi Transactions on Technopreneurship (ATT) 5, no. 3 (October 23, 2023): 240–50. http://dx.doi.org/10.34306/att.v5i3.354.
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The rapid development of digital technology and artificial intelligence (AI) has brought significant changes in many areas of life, including business, healthcare, education, and entertainment. The great potential of AI technologies to provide positive benefits to society is balanced with concerns about their negative impact on quality of life. This study aims to explore the influence of digital AI technology on the overall quality of life around the world, focusing on the Performance Expectancy, Effort Expectancy, and Use Behavior aspects of digital AI technology. This research method utilizes the UTAUT (Unified Theory of Acceptance and Use of Technology) approach and collects data quantitatively through a questionnaire covering 3 variables related to digital AI technology and its influence on quality of life. Data analysis was conducted using PLS-SEM (Partial Least Squares - Structural Equation Modeling) to identify important aspects related to the advantages and disadvantages of AI technology. The results from the 70 respondents indicated that digital AI technologies have the potential to improve quality of life by meeting performance expectations and providing ease of use, and a balanced approach is needed in the development and implementation of AI technologies to maximize their positive impact while minimizing their negative impact.
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Palamar, Svitlana, and Maryna Naumenko. "Artificial Intelligence in Education: Use Without Violating the Principles of Academic Integrity." Educological discourse 44, no. 1 (2024): 68–83. http://dx.doi.org/10.28925/2312-5829.2024.15.
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The article substantiates the relevance of issues related to the development of artificial intelligence, which allowed to form a clear understanding of modern AI tools. The prerequisites for the emergence and features of artificial intelligence as an international product are summarized. The author analyzes current trends in the field of artificial intelligence technologies. A list of popular AI technologies is presented and the current state of application of AI technologies by higher education students is determined. The article presents the results of a survey of higher education students of the Faculty of Pedagogical Education of Borys Grinchenko Kyiv Metropolitan University. The peculiarities of the use of artificial intelligence technologies by higher education students are determined. The advantages and negative consequences of the use of artificial intelligence in the education system are considered. The key issues related to the ethics of using AI in accordance with the principles and norms of academic integrity are described.
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Figueroa-Quiñones, Joel, Juan Ipanaque-Neyra, Heber Gómez Hurtado, Oscar Bazo-Alvarez, and Juan Carlos Bazo-Alvarez. "Development, validation and use of artificial-intelligence-related technologies to assess basic motor skills in children: a scoping review." F1000Research 12 (December 18, 2023): 1598. http://dx.doi.org/10.12688/f1000research.138616.1.
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Background: In basic motor skills evaluation, two observersers can eventually mark the same child’s performance differently. When systematic, this brings serious noise to the assessment. New motion sensing and tracking technologies offer more precise measures of these children’s capabilities. We aimed to review current development, validation and use of artificial intelligence-related technologies that assess basic motor skills in children aged 3 to 6 years old. Methods: We performed a scoping review in Medline, EBSCO, IEEE and Web of Science databases. PRISMA Extension recommendations for scoping reviews were applied for the full review, whereas the COSMIN criteria for diagnostic instruments helped to evaluate the validation of the artificial intelligence (AI)-related measurements. Results: We found 672 studies, from which 12 were finally selected, 7 related to development and validation and 5 related to use. From the 7 studies, we tracked 10 other publications updating and/or using these technologies. Engineering work and technological features have been prioritised in studies about AI-related technologies. The validation of these algorithms was strictly based on engineering criteria; it means, no substantive knowledge of the medical or psychological aspects of motor skills was integrated into the validation process. Technical features typically evaluated in psychometric instruments designed for assessing motor skills in children were also ignored (e.g., COSMIN criteria). The use of these AI-related technologies in scientific research is still limited. Conclusion: Clinical measurement standards have not been integrated into the development of AI-related technologies for measuring basic motor skills in children. This compromises the validity, reliability and practical utility of these tools, so future improvement in this type of research is needed.
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Getchell, Kristen M., Stephen Carradini, Peter W. Cardon, Carolin Fleischmann, Haibing Ma, Jolanta Aritz, and James Stapp. "Artificial Intelligence in Business Communication: The Changing Landscape of Research and Teaching." Business and Professional Communication Quarterly 85, no. 1 (February 3, 2022): 7–33. http://dx.doi.org/10.1177/23294906221074311.
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The rapid, widespread implementation of artificial intelligence technologies in workplaces has implications for business communication. In this article, the authors describe current capabilities, challenges, and concepts related to the adoption and use of artificial intelligence (AI) technologies in business communication. Understanding the abilities and inabilities of AI technologies is critical to using these technologies ethically. The authors offer a proposed research agenda for researchers in business communication concerning topics of implementation, lexicography and grammar, collaboration, design, trust, bias, managerial concerns, tool assessment, and demographics. The authors conclude with some ideas regarding how to teach about AI in the business communication classroom.
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VANKOV, V. V., O. R. ARTEMOVA, O. E. KARPOV, A. V. MATVIENKO, A. V. GUSEV, I. M. ENIKEEV, and E. V. KOSTINA. "RESULTS OF THE IMPLEMENTATION OF ARTIFICIAL INTELLIGENCE IN THE RUSSIAN HEALTHCARE." Vrach I informacionnye tehnologii, no. 3 (2024): 32–43. http://dx.doi.org/10.25881/18110193_2024_3_32.
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Introduction. The introduction of artificial intelligence (AI) technologies in Russian healthcare is an important step to improve the efficiency and quality of medical care. The development of AI technologies helps automate data processing, support physician decision-making and improve predictive analytics. Aim. Analysis of the results of creation and implementation of software solutions using AI technologies in Russian healthcare. Materials and Methods: A systematic search for data in the State Register of Medical Devices and on the official website of the Unified Information System in the field of procurement was conducted. The main research methods were analysis of registered AI-based medical devices and monitoring of their use in medical institutions. Results. Quantitative indicators of implementation of solutions using AI technologies in Russian healthcare have been determined. The factors facilitating and hindering the introduction of innovations were formulated. The list of components of the methodology of implementation and operation of medical solutions based on AI technologies, related changes in the organization of medical care, personnel training, and patients' involvement in the development of their health has been defined. Conclusions. Significant progress in the use of AI in Russian healthcare requires further disclosure of methodological, organizational, technological and economic issues. Continued sharing of regional practices and knowledge will be key to building trust in AI technologies.
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Simeonov, Svilen, Firgan Feradov, Angel Marinov, and Tamer Abu-Alam. "Integration of AI Training in the Field of Higher Education in the Republic of Bulgaria: An Overview." Education Sciences 14, no. 10 (September 27, 2024): 1063. http://dx.doi.org/10.3390/educsci14101063.
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The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor’s and 239 master’s degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future.
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Daugbjerg, Signe, Rossella Di Bidino, and Americo Cicchetti. "OP31 Assessment Of AI Supported Health Technologies - How To Move Forward?" International Journal of Technology Assessment in Health Care 38, S1 (December 2022): S13. http://dx.doi.org/10.1017/s0266462322000885.
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IntroductionArtificial intelligence (AI)-supported technologies are rapidly developing and have the potential to improve healthcare quality at reduced cost. However, few examples exist of successfully deployed AI-technologies in a real-world context that have been adequately assessed. Therefore, the objective of this research is to: (i) identify existing health technology assessment (HTA) methods developed or adapted to assess AI-supported health technologies, (ii) identify new assessment topics or domains relevant for AI-technology uptake, and (iii) take the first step in developing a framework applicable for new challenges that emerge with the introduction of AI.MethodsA systematic literature review of studies describing methods or frameworks to assess AI-supported health technologies was performed on PubMed from January 2010 until February 2021. Furthermore, a web page search of international HTA agencies and international organizations such as the World Health Organization, Organziation for Economic and Co-ordination and Development, and the European Commission was performed to identify important aspects to consider when implementing and assessing AI technologies.ResultsNo assessment frameworks for AI technologies were identified from the systematic literature review or web page searches of international HTA agencies. Reports from international organizations highlight limitations or inability of most AI technologies to ‘explain’ their decision-making process (black box issue), leading to lack of trust in the technology that affects its adoption. It is recommended to put more emphasis on assessing transparency and ‘explainability’ of the AI solution as well as aspects of safety, ethical, legal, and social issues related to implementation and the development/training phase of the AI technology.ConclusionsThe results from this study uncover key gaps in frameworks posed for performing a systematic and holistic assessment of AI in a real-world context of health care. However, valuable information on relevant assessment aspects for AI-supported technologies have been identified.The results will form the basis for the development of a framework to assist decision-makers in assessing AI-supported technologies in a holistic manner for a responsible deployment – the HTA AI Framework.
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Lasker, Archit. "EXPLORING ETHICAL CONSIDERATIONS IN GENERATIVE AI." International Journal of Advanced Research 12, no. 04 (April 30, 2024): 531–35. http://dx.doi.org/10.21474/ijar01/18578.
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Generative AI, which encompasses a range of technologies such as generative adversarial networks (GANs), language models, and image generators, has shown remarkable progress in recent years. These technologies have the potential to revolutionize various fields, from art and entertainment to healthcare and education. However, along with these advancements come ethical considerations that must be carefully addressed. This research paper examines the ethical challenges posed by generative AI, including issues related to bias, privacy, misinformation, and intellectual property. It also discusses strategies for mitigating these risks and fostering the responsible development and deployment of generative AI technologies.
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Volante, Louis, Christopher DeLuca, and Don A. Klinger. "Leveraging AI to enhance learning." Phi Delta Kappan 105, no. 1 (August 28, 2023): 40–45. http://dx.doi.org/10.1177/00317217231197475.
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Artificial Intelligence (AI) technologies, such as ChatGPT, have ushered in a new digital era that presents formidable challenges related to cheating and plagiarism in the classroom. Although many school systems have reacted by banning AI language models and related applications, these technologies present opportunities to leverage formative assessment practices to enable students to demonstrate more complex and valued learning outcomes. Using the ICE (Ideas, Connections, Extensions) model, Louis Volante, Christopher DeLuca, and Don A. Klinger offer educators a research-informed pathway, based on best practices and policy, to promote critical, creative, and higher order thinking in secondary schools.
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Shiyyab, Fadi Shehab, Abdallah Bader Alzoubi, Qais Mohammad Obidat, and Hashem Alshurafat. "The Impact of Artificial Intelligence Disclosure on Financial Performance." International Journal of Financial Studies 11, no. 3 (September 14, 2023): 115. http://dx.doi.org/10.3390/ijfs11030115.
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This study determines to what extent Jordanian banks refer to and use artificial intelligence (AI) technologies in their operation process and examines the impact of AI-related terms disclosure on financial performance. Content analysis is used to analyze the spread of AI and related information in the annual report textual data. Based on content analysis and regression analysis of data from 115 annual reports for 15 Jordanian banks listed in the Amman Stock Exchange for the period 2014 to 2021, the study reveals a consistent increase in the mention of AI-related terms disclosure since 2014. However, the level of AI-related disclosure remains weak for some banks, suggesting that Jordanian banks are still in the early stages of adopting and implementing AI technologies. The results indicate that AI-related keywords disclosure has an influence on banks’ financial performance. AI has a positive effect on accounting performance in terms of ROA and ROE and a negative impact on total expenses, which supports the dominant view that AI improves revenue and reduces cost and is also consistent with past literature findings. This study contributes to the growing body of AI literature, specifically the literature on AI voluntary disclosure, in several aspects. First, it provides an objective measure of the uses of AI by formulating an AI disclosure index that captures the status of AI adoption in practice. Second, it provides insights into the relationship between AI disclosure and financial performance. Third, it supports policymakers’, international authorities’, and supervisory organizations’ efforts to address AI disclosure issues and highlights the need for disclosure guidance requirements. Finally, it provides a contribution to banking sector practitioners who are transforming their operations using AI mechanisms and supports the need for more AI disclosure and informed decision making in a manner that aligns with the objectives of financial institutions.
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Umanets, Volodymyr, Iryna Shakhina, and Bohdan Rozputnia. "TRAINING FUTURE COMPUTER SCIENCE TEACHERS TO USE ARTIFICIALINTELLIGENCE TECHNOLOGIES IN THE EDUCATIONAL PROCESS." Modern Information Technologies and Innovation Methodologies of Education in Professional Training Methodology Theory Experience Problems, no. 72 (July 10, 2024): 162–69. http://dx.doi.org/10.31652/2412-1142-2024-72-162-170.
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The article discusses the training of future computer science teachers to use artificial intelligence (AI) technologies in education. The authors highlight the relevance of this issue in light of the rapid spread of AI technologies in various spheres of life and the need to develop students’ digital competencies related to the use of artificial intelligence. The study aims to identify the essential competencies that future computer science teachers need to effectively use AI in education. It also analyzes the existing problems and challenges in this area, studies the prospects for the use of AI in education, develops recommendations for improving relevant training programs, and overcoming barriers to the implementation of AI technologies. The study identified three main groups of competencies for future computer science teachers in the field of AI: technical, pedagogical, ethical and legal. Technical competencies include knowledge of AI technology principles, algorithms, and software. Pedagogical competencies relate to the ability to adapt teaching materials and methods to the capabilities of AI and to develop appropriate tasks and projects. Ethical and legal competencies involve comprehending the risks and challenges associated with AI usage, as well as being aware of the ethical principles and legal norms in this field. The article analyzes technical, pedagogical, ethical, and legal problems related to introducing AI technologies into computer science teacher training and education. It also explores the potential benefits of AI in education, such as personalized and adaptive learning, improved teaching and assessment effectiveness, and the development of critical thinking and creativity in students. The authors suggest methods to enhance computer science teacher training programs, such as incorporating specialized AI courses, providing practical training, and involving industry experts. They also stress the significance of considering the most effective international practices in this field. To summarize, the article highlights the pressing need to modernize the computer science teacher training system in Ukraine. This is necessary to develop the competencies required for the effective use of AI technologies in the educational process. Such modernization will contribute to the development of students’ digital literacy and ensure the competitiveness of the Ukrainian educational system in the context of digital transformation.
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Subbotina, M. V. "Artificial intelligence and higher education - enemies or allies." RUDN Journal of Sociology 24, no. 1 (March 15, 2024): 176–83. http://dx.doi.org/10.22363/2313-2272-2024-24-1-176-183.
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The development of artificial intelligence (AI) has become one of the most discussed topics in 2023. According to sociological surveys, the awareness of Russians in the field of AI and their willingness to use new technologies have grown. The leap in the development of neural networks, chatbots and AI technologies in general has already affected many areas of life, and the education system is no exception. Teachers face many challenges related to the regulation of the AI application in the educational process. On the one hand, issues of regulating the use of AI technologies in universities require a scrupulous study which is complicated by the speed of technological development. On the other hand, in addition to official regulations, it is necessary to solve more global and labor-intensive tasks: to unlock the potential of AI in the educational process; analyze the ethical side of the issue and develop the culture of using new technologies; adapt educational materials and assignments based on the possible application of AI by students; change curricula and revise the competency system, etc. The article considers ways to use AI technologies in the educational process as perceived by teachers and students. The author emphasizes both constructive and destructive capabilities of new technologies, the challenges that universities will face in the near future, and the positions of university representatives on these issues. The author believes that the use of AI technologies in education can benefit both teachers and students in sociology and other areas. It is impossible to stop the development of technologies; any attempts to hinder them are counterproductive; therefore, it is necessary to reconsider the established educational approaches according to the requirements of our time.
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Singh, Vijay, and Aastha Agnihotri. "Addressing Environmental Challenges through Artificial Intelligence (AI)-Powered Natural Disaster Management." International Journal of Applied and Scientific Research 2, no. 5 (May 31, 2024): 485–96. http://dx.doi.org/10.59890/ijasr.v2i5.1413.
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Recent advancements in AI offer promising tools for enhancing disaster management which is crucial given the increasing frequency of climate-related disasters. The study aims to evaluate how AI technologies can be utilized to improve disaster preparedness, response, and recovery efforts, thus aiding in environmental resilience and sustainability. This paper examines the intersection of artificial intelligence (AI) and environmental sustainability, with a focus on the role of AI in managing natural disasters. By reviewing secondary data and existing research, the paper explores various AI applications such as predictive modeling, real-time monitoring, and decision support systems. The analysis reveals that AI can significantly enhance early warning systems, optimize the allocation of resources, and ensure timely interventions during emergencies. The findings highlight the importance of integrating AI technologies into disaster management strategies to foster environmental sustainability amidst growing climate-related risks. The paper also discusses the challenges and ethical considerations of implementing AI in this field and underscores the need for interdisciplinary collaboration and stakeholder engagement for successful implementation.
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Kim, Minsang, Kyungyee Kim, Hyungwook Kim, and Jungeun Kim. "The Effect of Elementary School Artificial Intelligence Camp Programs on Attitudes Towards AI Technology and Data Literacy Skills." Korean Association For Learner-Centered Curriculum And Instruction 24, no. 6 (March 31, 2024): 15–25. http://dx.doi.org/10.22251/jlcci.2024.24.6.15.
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Objectives The aim of this study is to analyze the impact of an 8-session short-term Artificial Intelligence (AI) camp program utilizing Microsoft AI tools on the attitudes and data literacy skills of elementary school students towards AI technologies.
Methods To develop the AI camp program and validate its effectiveness, pre- and post-assessments were conducted on the attitudes and data literacy skills related to AI technologies among 1,141 students in grades 3-6 from elementary schools in Gyeonggi Province, South Korea. The assessments took place from April 3, 2023. The collected data were analyzed using the statistical software SPSS Statistics 26 through a paired-samples t-test.
Results The AI camp program was found to have a positive impact on the attitudes of the participating students towards AI technologies. The program overall increased the students' ambitions and interests in careers related to AI technologies. It was observed that students' understanding of the significance of AI and its societal impacts also improved. Some students showed awareness of the challenges in acquiring AI skills. These results suggest that the camp program has contributed to a shift in students’ attitudes and interests in a positive direction. Moreover, the students showed improvement in all areas of data literacy, including data comprehension, interpretation and evaluation, data management, and data utilization, indicating a positive influence of the camp program on their data literacy skills.
Conclusions In the future, it is expected that AI camps will not be one-off events but will be integrated into the semester-based curriculum across all subject areas. For such an educational strategy to be effectively implemented, it is necessary to establish a phased, long-term plan.
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Osadchuk, Evgeny. "Digitization of Industry: Barriers to the Creation of Artificial Intelligence and Proposals for Overcoming Them." Science Management: Theory and Practice 4, no. 2 (June 27, 2022): 201–9. http://dx.doi.org/10.19181/smtp.2022.4.2.17.
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The article, based on a series of studies conducted in 2021 by ANO “Digital Economy”, discusses the problems of digitalization of industry in Russia. The main goal of the conducted research is to find out how actively Russian companies use digital technologies, in particular, technologies related to artificial intelligence. The study covered several industries: woodworking industry, light industry, mechanical engineering, metallurgy, military-industrial complex, medical equipment production, production of socially important goods, pharmaceutical industry, chemical and petrochemical industry, electronic and microelectronic industry. Theobtained results show that a significant part of the companies almost does not use artificial intelligence (AI) technologies. The main barriers to the widespread use of AI in industry are also listed: these are infrastructure problems, a lack of qualified personnel, problems with data for AI, poor popularization of AI, and low return of investments of AI-using technologies. In conclusion, recommendations are given to overcome the identified barriers in the industrial sector.
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Kharbanda, Varun, Seetharaman A, and Maddulety K. "Journal." International Journal of Security and Privacy in Pervasive Computing 15, no. 1 (March 23, 2023): 1–13. http://dx.doi.org/10.4018/ijsppc.318676.
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Artificial intelligence (AI) has emerged as the most widely applicable field across varied industries. Being an evolving technology, it may be quite useful in sensitive areas such as cyber security where there is a dire need for implementation of AI technologies, such as expert systems, neural networks, intelligent agents, and artificial immune systems. The primary reason for AI fitment to cyber security area is its ability to detect anomalies proactively and predictively in the network, thereby working towards securing the network before the damage related to loss of data and/or reputation is done. There are different types of AI technologies as mentioned above that could be applied in cyber security in its varied forms. In this paper, the emphasis is on specific AI technologies that can bring unique benefits to the cyber security field with its unique applicability to different scenarios. The outcome of this study shows that AI technologies such as expert systems, neural networks, intelligent agents, and artificial immune systems are transforming the landscape for managing cyber threats.
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Lyapin, И. А. "The Impact of Artificial Intelligence on the Workplace: Current State and Future Perspectives." Journal of Digital Economy Research 1, no. 1 (March 12, 2023): 137–76. http://dx.doi.org/10.24833/14511791-2023-1-137-176.
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Rapid technological developments in Artificial Intelligence technologies and its multiple applications on the workplace have stoked many fears and speculations in the society about possible job loss. Current studies report no decrease in demand of high-skilled workers associated with AI technologies. Also, there are no evidences that they will displace jobs in the near future. More companies keep on using and scaling AI in nearly every industry. Most of the businesses are getting benefits after implementation of AI. Most of the progress AI made relate to highly-educated and skilled occupations. To develop further, companies should decide which tasks and routines to be performed and delegate them to humans or robots. AI will not only lead to automation, but will also complement labor. Implementation of AI technologies widely across all industries still face challenges. Part of the limitations is technical related, there are also potential issues with data protection, its fraudulent use and cybersecurity are the main challenges to focus. Businesses and authorities should harness AI development and benefit from the increased productivity and working performance. Focus should be on ensuring the transition to new technologies is smooth and stressless.
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Dymchenko, O., N. Matveeva, and Ye Kozyr. "EFFECTIVENESS OF IMPLEMENTING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN ENTERPRISE BUSINESS PROCESSES." Municipal economy of cities 5, no. 186 (September 6, 2024): 25–32. http://dx.doi.org/10.33042/2522-1809-2024-5-186-25-32.
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The article examines the effectiveness of artificial intelligence (AI) technologies in an enterprise’s business processes. In the beginning, the authors consider the essence of artificial intelligence and determine the theoretical basis of its impact on the activities of enterprises. Next, we analyse the dynamics and structure of the AI market. We determine AI as one of the most promising areas implemented in the companies’ business processes, allowing them to obtain significant savings in labour and financial resources. We provide information on the use of AI in enterprises’ activities by the economic sectors. The study establishes that AI is gaining traction in all sectors of the economy, and most of all in healthcare, manufacturing, and finance. Today, AI technologies create new opportunities for companies to provide them with broad powers in various industries. After all, every process implementing AI optimises costs and positively impacts the overall financial performance. We specify that companies need to develop a collaboration of people and technology that will complement each other and have a strong union of knowledge, speed, experience, and skills. The study shows that the introduction of AI has a positive impact on the level of profitability of companies because, with the popularisation of AI in 2022, more global companies began to implement these technologies in their business processes, and companies that use these technologies became in demand in the market, which in turn had a positive impact on profit growth. The study resulted in proposals for using artificial intelligence technologies in the business processes of Ukrainian enterprises. By implementing AI in their business processes, enterprises will receive significant savings in their resources, both human and financial. It is necessary to note that the effectiveness of AI will depend on its collaboration with humans; the technology can be a good solution in a situation where artificial intelligence handles some of the functions related to the processing of a data set, and people use the results obtained in this way as the basis for final decision-making. Keywords: artificial intelligence, business process, enterprise, technology, management.
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Lee, DonHee, and Seong No Yoon. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges." International Journal of Environmental Research and Public Health 18, no. 1 (January 1, 2021): 271. http://dx.doi.org/10.3390/ijerph18010271.
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This study examines the current state of artificial intelligence (AI)-based technology applications and their impact on the healthcare industry. In addition to a thorough review of the literature, this study analyzed several real-world examples of AI applications in healthcare. The results indicate that major hospitals are, at present, using AI-enabled systems to augment medical staff in patient diagnosis and treatment activities for a wide range of diseases. In addition, AI systems are making an impact on improving the efficiency of nursing and managerial activities of hospitals. While AI is being embraced positively by healthcare providers, its applications provide both the utopian perspective (new opportunities) and the dystopian view (challenges to overcome). We discuss the details of those opportunities and challenges to provide a balanced view of the value of AI applications in healthcare. It is clear that rapid advances of AI and related technologies will help care providers create new value for their patients and improve the efficiency of their operational processes. Nevertheless, effective applications of AI will require effective planning and strategies to transform the entire care service and operations to reap the benefits of what technologies offer.
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Kim, Myeongju, Hyoju Sohn, Sookyung Choi, and Sejoong Kim. "Requirements for Trustworthy Artificial Intelligence and its Application in Healthcare." Healthcare Informatics Research 29, no. 4 (October 31, 2023): 315–22. http://dx.doi.org/10.4258/hir.2023.29.4.315.
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Objectives: Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be actively used in actual clinical settings. Ensuring reliability is essential to disseminating technologies, necessitating a wide range of research and subsequent social consensus on requirements for trustworthy AI.Methods: This review divided the requirements for trustworthy medical AI into explainability, fairness, privacy protection, and robustness, investigated research trends in the literature on AI in healthcare, and explored the criteria for trustworthy AI in the medical field.Results: Explainability provides a basis for determining whether healthcare providers would refer to the output of an AI model, which requires the further development of explainable AI technology, evaluation methods, and user interfaces. For AI fairness, the primary task is to identify evaluation metrics optimized for the medical field. As for privacy and robustness, further development of technologies is needed, especially in defending training data or AI algorithms against adversarial attacks.Conclusions: In the future, detailed standards need to be established according to the issues that medical AI would solve or the clinical field where medical AI would be used. Furthermore, these criteria should be reflected in AI-related regulations, such as AI development guidelines and approval processes for medical devices.
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Paliukas, Vytautas, and Asta Savanevičienė. "Harmonization of rational and creative decisions in quality management using AI technologies." Economics and Business 32, no. 1 (November 1, 2018): 195–208. http://dx.doi.org/10.2478/eb-2018-0016.
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Abstract Artificial Intelligence (AI) systems are rapidly evolving and becoming more common in management. Managers in business institutions are faced with the decision taking challenges and large amounts of data to be processed combining and harmonizing rational data with creative human experience in decision making. The aim of the study is to reveal the main obstacles of the harmonization of creative and rational decisions making in quality management using AI technologies in the Quality Management System (QMS). The first section presents a literature review of approaches and trends related to AI technology usage in organisations for data processing and creative-rational decision making, rational and creative quality management decision making and paradigms in decision harmonization. The Main Results section presents practical analysis and testing experience of automated AI Quality Management System developed at a higher education institution. During the analysis, an interview method was applied to find out specific system implementation issues. In the last section, the main analysis results and further development possibilities are discussed. The main findings and conclusions disclose two main problematic areas which may be defined as obstacles for rational and creative management decisions in quality management, related with clear responsibility distribution and assignment between data inputters and experience interpreters and duplicated qualitative data which AI system is not capable of rationalizing at the present development stage, speech and language processing techniques used when data processing algorithms cannot cope with the dual data processing technique, because in practice the system interprets and rationalizes only one category of data either quantitative - based on rational defined indicators, or qualitative, based on language recognition and speech related data interpretation. Managers’ experience in harmonizing creative human experience in organisation’s quality management was evaluated as positive. Data processed by tested AI system allows for rationalization of creative experience with ready quantitative data output from QMS system and final harmonized strategic quality management decisions.
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Coimbra, Elisa Mara, and Flávio Luiz de Aguiar Lôbo. "Public foment for innovation in artificial intelligence: an assessment based on technological data from patents." International Journal of Digital Law 2, no. 3 (December 15, 2021): 11–26. http://dx.doi.org/10.47975/ijdl.coimbra.v.2.n.3.
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This paper aims to study the Brazilian public policy for fomenting innovation in Artificial Intelligence (AI), presenting the initial premise (hypothesis), to be inductively tested, that its greatest challenge is related to the endogenization or internalization of the processes of development and production of AI-related technologies in the country. To this end, we analyze data from the patenting of these technologies in Brazil, the most widely used indicator to measure national technological innovation, contrasting them with international data. Considering the diversity of formats that such public policies can take, this work reveals its importance, since it provides an accurate diagnosis of the reality in the AI segment in Brazil, a fundamental subsidy for the formulation of efficient planning.
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Fehler, Włodzimierz, Agnieszka Araucz-Boruc, Andrzej Dana, and Anna Lasota-Kapczuk. "Systemy sztucznej inteligencji jako wyzwanie dla sfery bezpieczeństwa i obronności RP." Zeszyty Prawnicze Biura Analiz Sejmowych 2, no. 70 (2021): 273–98. http://dx.doi.org/10.31268/zpbas.2021.39.
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The authors pointed out the basic challenges related to the use of artificial intelligence (AI) in the area of security and defence of the Republic of Poland. Challenges to Polish security and defence related to modern technologies, including the use of AI systems, arise from the fact that Poland remains outside the leading group of highly technologically advanced countries. It is necessary to implement organisational measures to strengthen and expand the technological potential of the state, above all the development of national solutions in the sector of new technologies and cyber security, and the creation of a comprehensive document that defines the strategic objectives in the area of AI use and the ways of their implementation.
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Valko, Nataliia V., Tatiana L. Goncharenko, Nataliya O. Kushnir, and Viacheslav V. Osadchyi. "Cloud technologies for basics of artificial intelligence study in school." CTE Workshop Proceedings 9 (March 21, 2022): 170–83. http://dx.doi.org/10.55056/cte.113.
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Changes in society related to the development of science, technology, computing power, cloud services, artificial intelligence, increasing general access to huge amounts of open data, lead to increased global investment in technology and services. Appropriate training is required by specialists to create a workforce to work with artificial intelligence. On the one hand, it puts forward new requirements for the training of young people, and educational content, on the other hand, provides opportunities for the use of cloud technologies during the educational process. Widespread use of AI in various fields and everyday life poses the task of understanding the basic terms related to Artificial intelligence (AI), such as Machine learning (ML), Neural network (NN), Artificial neural networks (ANN), Deep Learning, Data Science, Big Data, mastering the basic skills of using and understanding the AI principles, which is possible during the study in the school course of computer science. Cloud technologies allow you to use the power of a remote server (open information systems, digital resources, software, etc.) regardless of the location of the consumer and provide ample opportunities for the study of artificial intelligence. In this article we reveal the possibilities of cloud technologies as a means of studying artificial intelligence at school, consider the need for three stages of training and provide development of tasks and own experience of using cloud technologies to study artificial intelligence on the example of DALL-E, Google QuickDraw, cloud technologies Makeblock, PictoBlox, Teachable Machine at different stages of AI study.
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Baksh, Hari, Kavya Thottempudi, Manjit M. Khatal, Sujatha G S, Nikita Das, Mohd Aftab Alam, Rajshree Karanwal, and Priya P. "Advancing Agriculture through Artificial Intelligence, Plant Disease Detection Methods, Applications, and Limitations." Journal of Advances in Biology & Biotechnology 27, no. 8 (July 31, 2024): 730–39. http://dx.doi.org/10.9734/jabb/2024/v27i81191.
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In recent years, the integration of artificial intelligence (AI) into agriculture has transformed traditional farming practices. One area of significant advancement is in the detection of plant diseases, where AI-driven technologies offer innovative solutions to mitigate crop losses and enhance agricultural productivity. This paper explores the latest methodologies, applications, and challenges in utilizing AI for plant disease detection. We review various AI techniques, including machine learning, computer vision, and deep learning, that have been deployed to accurately identify and diagnose plant diseases. Additionally, we discuss the practical applications of these technologies in real-world agricultural settings, highlighting their potential to revolutionize crop management practices. Despite the promising developments, we also address the limitations and obstacles faced in implementing AI-based plant disease detection systems, including issues related to data quality, model generalization, and scalability. By critically examining the current landscape of AI-driven plant disease detection, this paper aims to provide insights for researchers, practitioners, and policymakers to further advance the integration of AI technologies in agriculture.
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Pitukhina, Maria A., and Anastasiya D. Belykh. "Artificial Intelligence Technologies in the Russian Arctic: The Case of the Murmansk Oblast." Arctic and North, no. 52 (September 29, 2023): 167–79. http://dx.doi.org/10.37482/issn2221-2698.2023.52.167.
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Russian Arctic is a positive example of the introduction of information technologies (Industry 4.0.) as well as artificial intelligence technologies (Industry 5.0.). In the 21st century, IT-technologies have significantly improved quality of life in the Russian Arctic — development of IT camps, access to the Internet from the tundra. Arctic projects related to the AI technologies implementation are becoming increasingly popular: the article provides a list of such Arctic AI projects. An analysis of IT and AI vacancies in all subjects of the Russian Arctic on the website of the headhunter recruitment agency showed that the largest number of IT vacancies was posted directly in the Murmansk Oblast (74 vacancies). The study also analyzed job seek-ers’ resumes in the Murmansk Oblast, posted in the Artificial Intelligence section. The study shows that knowledge of Python programming language, SQL databases and English language is a prerequisite for all AI specialists. It was also determined that the salary of AI specialists is significantly higher than that of IT specialists. The Murmansk Oblast is becoming a leader in the development and implementation of both IT and AI technologies; this is primarily due to the development of logistics and the Northern Sea Route as an alternative to existing sea routes.
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Justina Eweala Abubakar, Samuel Omokhafe Yusuf, Godbless Ocran, Peprah Owusu, Prosper Onagie Yusuf, and Adedamola Hadassah Paul-Adeleye. "Exploring the impact of AI-driven and blockchain-enabled tax filing systems on smes in the era of technological innovation: A review of benefits, challenges, and adoption barriers." World Journal of Advanced Research and Reviews 23, no. 3 (September 30, 2024): 1867–78. http://dx.doi.org/10.30574/wjarr.2024.23.3.2754.
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The emergence of blockchain technology and artificial intelligence (AI) presents transformative opportunities for tax compliance, especially for small and medium-sized enterprises (SMEs). These technologies promise to enhance tax system accuracy, efficiency, and transparency. However, their adoption is accompanied by significant challenges and barriers. This review analyzes the benefits, challenges, and future prospects of integrating AI and blockchain technologies into tax systems, with a focus on their impact on SMEs. It seeks to provide insights into how these technologies can reshape tax compliance and identify areas requiring further research. The review revealed that AI enhances accuracy and compliance through advanced predictive models and explainable AI, while blockchain ensures transparency and trust with its immutable ledger and smart contracts. Despite these benefits, SMEs face technical and financial constraints, such as high implementation costs and integration complexities. Regulatory and legal challenges, including evolving tax laws and data privacy requirements, further complicate adoption. Additionally, resistance to new technologies and skill gaps within SMEs hinder widespread implementation. Scalability and customization issues also pose significant barriers. In conclusion, AI and blockchain technologies substantially improve tax compliance but come with notable challenges. These technologies promise enhanced accuracy and efficiency for SMEs, yet overcoming barriers related to cost, integration, and regulation is essential for successful adoption. It was recommended that future research should focus on developing SME-specific solutions, addressing regulatory adaptations, and conducting comparative analyses of traditional versus AI-driven tax systems. By tackling these areas, stakeholders can better support SMEs in leveraging AI and blockchain technologies for more effective and transparent tax compliance.
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Du, Hua-Qing, Zu-Hui Zhang, Chen-Chen Wang, Jing Zhai, Wei-Hua Yang, and Tie-Pei Zhu. "Artificial intelligence-aided diagnosis and treatment in the field of optometry." International Journal of Ophthalmology 16, no. 9 (September 18, 2023): 1406–16. http://dx.doi.org/10.18240/ijo.2023.09.06.
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With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice.
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Shimpo, Fumio. "The Importance of ‘Smooth’ Data Usage and the Protection of Privacy in the Age of AI, IoT and Autonomous Robots." Global Privacy Law Review 1, Issue 1 (March 1, 2020): 49–54. http://dx.doi.org/10.54648/gplr2020006.
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The emerging technologies of AI and autonomous robots are forcing us to consider not only improvements in the development of their industrial use but also further urgent research into the ethical and legal issues. In the future, autonomous robots equipped with AI will become more widespread in our society and such robot acquisition of data may lead to data confidentiality issues which we are not able to solve just by focusing solely on AI-data acquisition issues. This paper focuses on the possibilities of privacy violation and the issues which should be considered related to handling personal data and focuses on an introduction to the Japanese Personal Information Protection Act, the mutual adequacy findings between Japan and the EU, the Data Free-Flow with Trust (DFFT) initiative and future legal discussions about the increasing use of AI. Finally, I will point out the need to both clarify and streamline any related future regulations. AI, autonomous robots, IoT, Japan, emerging technologies
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Gu, Jiasheng, Chongyang Gao, and Lili Wang. "The Evolution of Artificial Intelligence in Biomedicine: Bibliometric Analysis." JMIR AI 2 (December 19, 2023): e45770. http://dx.doi.org/10.2196/45770.
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Background The utilization of artificial intelligence (AI) technologies in the biomedical field has attracted increasing attention in recent decades. Studying how past AI technologies have found their way into medicine over time can help to predict which current (and future) AI technologies have the potential to be utilized in medicine in the coming years, thereby providing a helpful reference for future research directions. Objective The aim of this study was to predict the future trend of AI technologies used in different biomedical domains based on past trends of related technologies and biomedical domains. Methods We collected a large corpus of articles from the PubMed database pertaining to the intersection of AI and biomedicine. Initially, we attempted to use regression on the extracted keywords alone; however, we found that this approach did not provide sufficient information. Therefore, we propose a method called “background-enhanced prediction” to expand the knowledge utilized by the regression algorithm by incorporating both the keywords and their surrounding context. This method of data construction resulted in improved performance across the six regression models evaluated. Our findings were confirmed through experiments on recurrent prediction and forecasting. Results In our analysis using background information for prediction, we found that a window size of 3 yielded the best results, outperforming the use of keywords alone. Furthermore, utilizing data only prior to 2017, our regression projections for the period of 2017-2021 exhibited a high coefficient of determination (R2), which reached up to 0.78, demonstrating the effectiveness of our method in predicting long-term trends. Based on the prediction, studies related to proteins and tumors will be pushed out of the top 20 and become replaced by early diagnostics, tomography, and other detection technologies. These are certain areas that are well-suited to incorporate AI technology. Deep learning, machine learning, and neural networks continue to be the dominant AI technologies in biomedical applications. Generative adversarial networks represent an emerging technology with a strong growth trend. Conclusions In this study, we explored AI trends in the biomedical field and developed a predictive model to forecast future trends. Our findings were confirmed through experiments on current trends.
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Donia, Joseph, and James A. Shaw. "Co-design and ethical artificial intelligence for health: An agenda for critical research and practice." Big Data & Society 8, no. 2 (July 2021): 205395172110652. http://dx.doi.org/10.1177/20539517211065248.
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Applications of artificial intelligence/machine learning (AI/ML) in health care are dynamic and rapidly growing. One strategy for anticipating and addressing ethical challenges related to AI/ML for health care is patient and public involvement in the design of those technologies – often referred to as ‘co-design’. Co-design has a diverse intellectual and practical history, however, and has been conceptualized in many different ways. Moreover, AI/ML introduces challenges to co-design that are often underappreciated. Informed by perspectives from critical data studies and critical digital health studies, we review the research literature on involvement in health care, and involvement in design, and examine the extent to which co-design as commonly conceptualized is capable of addressing the range of normative issues raised by AI/ML for health care. We suggest that AI/ML technologies have amplified and modified existing challenges related to patient and public involvement, and created entirely new challenges. We outline three pitfalls associated with co-design for ethical AI/ML for health care and conclude with suggestions for addressing these practical and conceptual challenges.
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Sharma, Anuwanshi, and Mayank Pandey. "The influence of artificial intelligence technologies in healthcare systems and rise in their applications." IP International Journal of Forensic Medicine and Toxicological Sciences 8, no. 3 (October 15, 2023): 88–93. http://dx.doi.org/10.18231/j.ijfmts.2023.020.
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Numerous industries, including healthcare, have seen an increase in the use of artificial intelligence (AI). All types, sizes, and specialties of healthcare companies arebecoming more interested in how artificial intelligence has evolved and is aiding with patient needs and care, reducing costs, and enhancing efficiency. This review includes various research studies that used AI models in several healthcare domains, including dermatology, radiology, medication design, etc. It also analyses the effects of AI on healthcare administration and the challenges of using AI in healthcare. Artificial intelligence (AI) has recently advanced quickly in hardware implementation, software algorithms, and applications across various industries. Including illness diagnosis, living aid, processing of biomedical information, and biomedical research. This study aims to maintain the pace of recent scientific advances, comprehend the state of technology, recognize AI's enormous potential in biomedicine, and inspire researchers in related disciplines.