Artificial Intelligence Copyright Analysis and Fiduciary Considerations

Artificial Intelligence (AI) is being increasingly used for equid health and welfare. This study aimed to establish consensus on where and how AI should be developed to achieve maximum benefit in this field. A workshop involving 41 stakeholders generated statements about current welfare concerns, areas for AI development, and barriers and solutions to AI use. Statements were circulated through Delphi surveys (acceptance set at 75% agreement). One-hundred-and-six statements reached agreement. Ethological needs not being met and poor equid management practices were key welfare concerns. Participants identified that insufficient owner/carer knowledge and understanding were important factors contributing to welfare concerns. Priority areas for AI development included assessment of equid wellbeing, as well as individual and population-level monitoring. Barriers included limited understanding of both equine behaviour and AI, biased, unethical, or insufficient data collection, difficulties developing accurate models, challenges to validation, and uncertainty around interpretation. Proposed solutions included development of evidence-based, unbiased AI systems, following best practice guidelines, requiring approval/regulation of AI tools, collaboration, and education of AI users. This is the first study to identify stakeholders' opinions about where AI is likely to have the greatest benefit for equids, potential barriers, and solutions. The findings should be used to prioritise funding and development.

Traditional birth attendants (TBAs) have become an integral part of the workforce providing delivery services in Nigeria due to the limited number of skilled birth attendants and cultural preferences. This study assessed the knowledge, management and preventive practices regarding postpartum haemorrhage (PPH) among TBAs in selected communities in Osun State, Southwest Nigeria. The study employed a descriptive cross-sectional study design and recruited 260 TBAs in four communities in Osun State. Data were collected by means of an adapted semi-structured questionnaire. Data were analysed using SPSS version 23 and summarized using descriptive and inferential statistics (chi-square and logistic regression) with the level of significance set at p < 0.05. The findings indicated that most (71.4%) of the TBAs were cleric, while others were herbalist (28.6%). Although the majority (76.4%) of the TBAs had good knowledge of the causes and warning signs of PPH, a high percentage (69.3%) of TBAs had poor management practices, while 114 (64.1%) TBAs had inadequate preventive practices. Notably, almost none of the participants practised active management of the third stage of labour; the majority of TBAs did not administer any uterotonic drugs to the mother, nor did they deliver the placenta by controlled cord traction. Gender (P = 0.029), educational level (P = 0.035) and average number of births per month (P = 0.001) significantly influenced TBAs’ management practices. Similarly, the TBA type (P < 0.001), average number of births per month (P = 0.003) and experience with formal training (P = 0.005) showed significant associations with TBAs’ preventive practices. Furthermore, TBAs’ preventive practices towards PPH were influenced by the TBA type (OR: 4.23; 95% CI 1.64–10.90). TBA management practices were also influenced by the TBA type (OR: 4.42; 95% CI 2.03–9.61). Traditional birth attendants in this study had poor management and poor preventive practices for postpartum haemorrhage.

Urgency of the research of artificial intelligence research in a digital society presents numerous causal changes, among them: space research, biotechnology, nanotechnology, digital technology, etc. The implications for human beings and human civilisation are significant as they affect a number of already existing contemporary issues such as economics, politics, ethics, law, psychology, conflictology, sociology, and ecology. Anthropology has to return to the existence of human beings in a society of artificial intelligence, consciousness and even the physical body. The study conceptualises artificial intelligence as a factor of ecosystem growth in the context of technological, ontological, and anthropological dimensions. The research objectives: 1) to theorise artificial intelligence as a potential and resource for use in all sectors of the socioeconomic sphere in the context of technological dimensions; 2) to clarify the convergence of artificial intelligence and its main components in the context of ontological dimensions; 3) to reveal the role of artificial intelligence in enhancing the digitalisation of society and humans in the context of anthropological dimensions. In the research we are guided by the works of scholars who have studied and are studying the problems of artificial intelligence as a complex social, economic, and cultural phenomenon: R. Andriukaitienė, N. Boström, A. Vance, P. Diamandis, S. Kotler, P. Dickson, L. Kai-Fu, and K. Kell, K. Kell, and others. Research methodology. The theory of artificial intelligence is a complex multidisciplinary science, which is located at the junction of economics, sociology, computer science, mathematics, psychology. Summarizing the processes of interdisciplinary interaction of artificial intelligence, we note that its study requires an interdisciplinary approach based on the synthesis of scientific knowledge, as revealing the content and principles of functioning of a complex invariant system of artificial intelligence. To analyze the modern model of artificial intelligence, it is necessary to apply a systematic institutional-evolutionary approach. By analysing different methodological approaches, it is necessary to acknowledge the presence of an anthropogenic component, as the methodology of scientific research is based on the method of information-anthropogenic analysis based on the fundamental position that no artificial intelligence processes are possible without information/data as an object and human as a subject of cognition. In addition, the following methods were used: induction, deduction, sublimation, synergetics, evolutionary historicism, modelling, and forecasting. The result of the research. Artificial Intelligence (AI) is a broad term used to define technologies/ engineering systems that emulate human intelligence, linking to neurophysiology, robotics, psychology (pattern recognition, modelling of psychological processes), transhumanism, cybernetics (computing power to find patterns in large data sets). Artificial Intelligence has been defined as a software-engineered system that is capable of influencing the environment with varying degrees of autonomy, producing results (predictions, recommendations, decisions) for a specific set of goals for the digitalisation of society. On the other hand, artificial intelligence causes many problems that are seen as cyber/virtual addiction, loss of reality, increased surveillance, loss of protocol control, privacy issues, cybersecurity, etc. The convergence of large data with artificial intelligence has become the single most important development shaping the future of social institutions in the digital society and the prospects for realising potential opportunities. Thanks to the latest digital technologies, advances in software engineering, and evolving artificial intelligence, there are both new perspectives for solving the most complex problems of modern civilization and new challenges in the technological, ontological, and anthropological dimensions.

The use of ERP systems is becoming an integral part of business information support and allows enterprises to rationalize the use of resources. The purpose of the article is an in-depth analysis of the use of ERP systems in the activities of enterprises, identification of the advantages and limitations of their practical application, which allow the development of ERP systems on the basis of technological, organizational and cognitive convergence to meet the changing needs of modern business. The study used the methods of case analysis, synthesis, and graphical visualization of information. The paper develops the applied aspect of certain areas of modern institutional economic theory, in particular the theory of evolutionary economics. Attention is focused on how the factor of technological progress affects the transformation of company management due to the principle of bounded rationality. The study summarizes the practice of using ERP systems and analyzes the relationship with the processes of convergence of information, communication, management, and cognitive technologies. The factors of rational use of resources based on ERP system data for four types of operating models of companies are identified. They are identified depending on the level of standardization and integration of business processes. Integration contributes to the rational use of resources through the automation of routine processes but requires several business units to use common practices for working with information in terms of its receipt, registration, storage, confidentiality, and distributed access. Standardization brings cost savings and efficiency by unifying and speeding up business processes but limits the ability to transform individual processes to adapt to rapid changes in the environment. The results of the study will help enterprises when implementing or transforming ERP systems to determine their desired configuration, in accordance with business needs, while maintaining the required level of adaptability of business processes. The results will also be useful for researchers conducting comprehensive studies of the impact of information technology on business operations. Technological advances mean that ERP systems continue to evolve and integrate with other technologies, such as artificial intelligence, the Internet of Things, and machine learning, to provide businesses with valuable context that transforms information into unique knowledge, which outlines the prospect for further research.

The World Meteorological Organisation defines climate as long-term weather patterns observed over decades or centuries, and these patterns have changed dramatically in recent years, mainly because of human activities like burning fossil fuels and cutting down forests. Artificial Intelligence (AI) has emerged as a transformative technology capable of addressing climate-related challenges across various sectors. This review explored the intersection of AI and climate change mitigation, highlighting how AI technologies such as machine learning, deep learning, natural language processing, and remote sensing contribute to monitoring emissions, forecasting weather, optimising energy systems, and supporting sustainable agriculture. The paper also discussed AI’s role in disaster risk management by enabling rapid data analysis and decision-making during emergencies. The findings revealed that AI's value in addressing climate change comes from its capacity to monitor environmental factors, forecast future conditions, and optimise resource use. Despite its immense potential, the deployment of AI in climate action faces several challenges, including high energy consumption, data scarcity, ethical concerns, and infrastructure limitations, particularly in under-resourced regions. Nevertheless, advancements in energy-efficient AI models, integration with IoT and satellite systems, and developments in decentralised learning techniques present significant opportunities for the future. This paper concluded that while AI is not a standalone solution, it is a critical component in the global climate mitigation strategy. With the right investments in education and research, AI has the potential to become a cornerstone of global climate action. Again, responsible implementation and cross-sector collaboration, AI can accelerate progress toward a more sustainable and climate-resilient future.

The incorporation of Artificial Intelligence (AI) within Industry 5.0 significantly enhances resilience among small businesses. This chapter explores how AI transforms resilience, sustainability, and customer engagement strategies. With AI Small businesses can analyze large datasets to identify risks, optimize operations, and deliver personalized experiences that align with consumer expectations. AI's ability to process data efficiently allows businesses to anticipate market changes and navigate uncertainties. Additionally, adopting AI fosters a culture of resilience, encouraging employees to embrace change. AI also supports sustainable practices by optimizing resource use and reducing waste. Customer engagement improves through AI-driven personalization, allowing businesses to tailor products and services to individual preferences. The chapter concludes with recommendations for small businesses: invest in employee training for AI collaboration, ensure leadership commitment, prioritize sustainability, and foster a culture of adaptability to thrive in today's Industry 5.0.

Artificial Intelligence (AI) is increasingly pivotal in addressing global challenges by integrating technological advancements into environmental conservation and societal development. This paper explores the dual nature of AI's role—its capacity to foster sustainability and societal improvement, alongside the challenges it introduces. The research highlights AI applications in environmental management, such as optimizing resource use, monitoring ecosystems, and enhancing renewable energy efficiency. In societal contexts, AI's transformative potential extends to healthcare, education, and urban planning, fostering equitable access to essential services. However, significant barriers, including data scarcity, limited technical infrastructure, regulatory gaps, and socio-economic inequalities, hinder widespread AI adoption. By analyzing secondary data from recent literature, this study identifies critical challenges and presents actionable policy recommendations to ensure inclusive, ethical, and impactful AI integration. The findings underscore the urgency of collaborative efforts among governments, industries, and academia to leverage AI for achieving the United Nations Sustainable Development Goals (SDGs) and addressing pressing global issues.

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POSB290 Artificial Intelligence in Medical Diagnostics: A Review of NICE MedTech Innovation Briefings

The integration of Artificial Intelligence (AI) in sustainability reporting represents a transformative approach of advancing Environment, Social, Governance (ESG) reporting. This study aims to explore how AI can enhance transparency, accuracy, and efficiency in sustainability disclosures, addressing increasing global demands for accountability in ESG disclosure. AI enables real-time data collection and analysis, allowing companies to track environmental impacts, optimize resource use, and reduce carbon emissions. Socially, AI promotes workforce diversity and inclusion through unbiased recruitment algorithms and improves workplace safety with predictive analytics in occupational health and safety. In governance, AI-driven risk management tools and blockchain technologies strengthen corporate governance by ensuring compliance and transparency in supply chains and financial reporting. The novelty of this study lies in its focus on AI-driven innovations that streamline data collection, automate reporting processes, and enable predictive analytics for anticipating environmental and social impacts. Using a qualitative research methodology, the study is grounded in an extensive literature review and analysis of secondary data, including academic journals, corporate sustainability reports, and regulatory publications. The findings reveal that AI significantly improves data accuracy and reporting efficiency while enabling organizations to meet compliance standards effectively. However, high implementation costs, data quality requirements, and ethical considerations are several challenges that should resolved. The study concludes that AI has the potential to revolutionize ESG reporting, provided organizations invest in robust data infrastructures and capacity-building initiatives. Future research should explore the development of standardized AI frameworks for sustainability reporting and the role of cross-sector collaborations in addressing implementation challenges.

[1]. Artificial intelligence (AI) in agriculture has the potential to revolutionize farming practices, improving productivity, sustainability, and decision-making. This study explores the application of AI in agriculture, focusing on how AI-driven tools and systems can optimize various farming processes, such as crop management, pest control, and yield prediction. The research evaluates the role of AI in addressing challenges faced by farmers and its impact on agricultural efficiency. The findings highlight the significant benefits of AI technologies in modernizing agriculture and creating sustainable farming solutions. Research Significance: The significance of this research lies in its ability to showcase the transformative potential of artificial intelligence in agriculture. AI applications can enhance precision farming, reduce resource wastage, and improve crop yields, all of which contribute to better sustainability and profitability for farmers. The study explores how AI technologies can address key challenges in agriculture, such as labor shortages, unpredictable weather patterns, and the need for optimized resource use. The findings offer valuable insights into how AI can drive agricultural innovation and help ensure food security in an increasingly volatile global environment. Methodology: COPRAS The COPRAS (Complex Proportional Assessment) methodology is utilized to assess and compare various AI-driven agricultural solutions and technologies. By evaluating multiple alternatives based on their effectiveness in improving agricultural practices, COPRAS provides a structured approach to selecting the most suitable AI solutions. The methodology takes into account factors such as cost-effectiveness, efficiency, and scalability, helping to identify the most impactful AI technologies for different agricultural needs. Alternative: AI-based crop monitoring systems, AI-driven irrigation technologies, AIpowered pest detection tools, AI-enhanced yield prediction models, AI-assisted supply chain management. Evaluation Parameters: Product price (C1), Company rating (C2), Delivery time (C3), Transportation costs (C4). Result: The results of the COPRAS analysis demonstrate that AI-driven technologies have the potential to significantly improve agricultural outcomes. AIpowered systems that offer efficient crop monitoring, precise irrigation, and early pest detection provide high value to farmers. Technologies with lower costs and faster deployment times, such as AI-based irrigation systems, tend to rank higher in the evaluation, making them more attractive to farmers seeking affordable solutions. However, the effectiveness and scalability of the solution are also crucial factors influencing the overall ranking

The advent of artificial intelligence (AI) presents a transformative opportunity to revolutionize agriculture and rural economies, offering a pathway to sustainable livelihoods. In precision agriculture, AI enables real-time monitoring of soil conditions, crop health, and weather patterns, and optimizes resource use through AI-driven irrigation and fertilization systems. AI-based image recognition and predictive models allow farmers for early detection of pest and disease outbreaks, leading to targeted interventions that reduce environmental impact and costs. In addition, supply chain management and market access benefit from AI algorithms that forecast market trends, optimize logistics and reduce post-harvest losses. Community empowerment is achieved by ensuring inclusive participation in AI solutions and creating job opportunities in AI and technology-related roles. To fully harness the potential of AI, supportive policies and robust infrastructure are essential, along with addressing ethical considerations such as data privacy and equitable access.

This chapter examines the transformative impact of Artificial Intelligence (AI) on sustainable resource management in the context of mounting global challenges such as population growth, urbanization, and climate change. By emphasizing the importance of innovative AI applications across key sectors such as water resource management, energy optimization, forest and wildlife conservation, and sustainable urban development, while providing clearer transitions to enhance reader comprehension, this chapter showcases the potential benefits of AI in achieving more efficient and equitable resource use. However, it also addresses critical challenges, including data limitations, energy consumption, and ethical considerations that must be navigated. Case studies exemplify successful AI implementations in resource management, integrating recent studies on graphically controlled metric spaces to strengthen the literature review, and discussing the moral and policy frameworks necessary to ensure responsible AI use. As AI continues to evolve, future directions emphasize collaboration and comprehensive integrative strategies to enhance sustainability efforts worldwide.

Today, Artificial Intelligence (AI) has a fairly wide application in various fields. Enterprises are no exception, where it is used in almost all business processes, and in particular in the planning of material resources. Such planning helps to increase production efficiency and optimize the use of resources. In this article, we will consider what resources are, their types and stages of planning. We will define what material resource planning is, its importance in the activities of the enterprise and the possible consequences of incorrect planning. AI as a modern solution to problems that arise in the process of enterprise activity in the areas of: material resource management, demand forecasting, procurement automation, inventory control and supply chain improvement, etc. In particular, we will consider what AI is, by what principles it works, its main advantages and capabilities in resource planning and in various processes of the organization's activity. We will find out what challenges the enterprise faces: the complexity of using and the cost of implementing the latest technologies, as well as the prospects for the development of AI in the field of resource provision. We will also consider SCM and ERP systems as tools for implementing AI in an enterprise, which provide automation of key processes, such as purchasing, accounting, supply and production chain management. We will pay attention to the issues of security and correctness of data provided by AI, the need to adapt business processes, attract qualified personnel and train them. We will review the dynamics of technology implementation in the world and Ukraine. The approaches proposed in the article can be useful for enterprises that seek to increase competitiveness in the modern world through digitalization and the use of advanced technologies. The experience of global and domestic companies, as well as the demand for solutions based on artificial intelligence, already today indicate a positive result of their implementation.

In public health computing, artificial intelligence (AI) and big data analytics together provide a wealth of fresh approaches to handle significant public health issues, enhance patient outcomes, and guide choices. Standard approaches of analysis may fail to provide real-time insights that can be utilised to move fast as the volume of data in healthcare systems all across the globe rises. Together, artificial intelligence (AI) and big data analytics can manage enormous volumes of various kinds of health data, including social aspects of health, public health data, and electronic health records (EHR). This combination allows one to build prediction models able to detect emerging illnesses, see health trends approaching, and identify groups of persons at risk. From vast volumes of data, artificial intelligence systems—including deep learning and machine learning—can identify helpful patterns. This clarifies risk factors, forecasts disease outbreaks, and guides choices on the most efficient use of resources. Moreover, Big Data analytics allows us to examine large-scale effects of activities, thereby enabling individuals in decision-making to do so grounded on strong evidence. By anticipating how each patient will do, thus improving treatments, and so reducing variations in access to and outcomes of healthcare, using AI and Big Data combined may also assist to personalise healthcare. Using AI and Big Data in public health informatics presents some challenges even with these advances. Concerns concerning data security, the requirement of uniform data formats, and the possibility that algorithms may produce biassed choices abound, for instance. Dealing with these problems is very crucial if we are to guarantee fair and ethical use of Big Data and artificial intelligence to enhance public health choices. This article discusses how Big Data analytics and artificial intelligence will transform public health informatics going forward. It lists their advantages and drawbacks and offers ideas for improving the responses on the pitch.