ARTIFICIAL INTELLIGENCE IN HUMAN LIFE: PERSON OR INSTRUMENT

Abstract. The problem of the quality of managerial decisions is one of the most acute problems of agriculture. Their quality can be improved with the use of digital technologies, including the use of artificial intelligence (AI) systems. The purpose of the study is to clarify the main stages of managerial decision-making, taking into account the use of AI systems. The scientific novelty lies in the development of a structural model for making a managerial decision, taking into account the use of AI systems, the main components of this process are identified. The research methods were the analysis of publications in the WoS scientific citation network on the topics “agriculture” and “artificial intelligence”, as well as the abstract-logical method in the analysis of the main stages of making a managerial decision. The results of the study were the determination of the composition and content of the stages of the procedural decision invariant, taking into account the use of artificial intelligence systems. The use of artificial intelligence systems allows diagnosing the occurrence of problems in crop production, animal husbandry, and technical systems at an early stage. Data collection and analysis in the process of making a managerial decision using AI systems includes direct data collection using sensors, cameras, scanners, etc., their cleaning and preliminary analysis, exploratory and statistical analysis, data modeling and interpretation of results. The use of AI systems will make it possible to operate with large data sets from agricultural production facilities, which will reduce uncertainty in making managerial decisions. The analysis of alternatives and the development of a management decision using AI systems turns off the forecasting of agricultural development indicators in a given system of constraints, the generation of alternative solutions and the choice of the optimal alternative, the acceptance or ignoring of the proposed alternatives. AI systems can be used to automate and optimize the process of implementing management decisions, monitoring and controlling management decisions. The use of AI systems to automate management decision-making processes in agriculture can help improve management efficiency.

The development of the digital revolution facilitates innovative models that generate new markets and business opportunities. The reappearance of artificial intelligence (AI) has created further potentials and types of market participation. AI is understood as a cutting-edge technology and a key driver of the transition of our economy into the digital economy. It is important to recognize and constantly bear in mind that artificial intelligence systems provide certain benefits but are associated with certain risks and potential negative effects. The European Commission, in its Ethical Guidelines for Trustworthy Artificial Intelligence (2019), emphasizes ethical principles and associated values that must be respected in the development, introduction, and use of artificial intelligence systems: respect for human autonomy, prevention of harm, fairness, and explainability. The question arises as to whether the emerging fundamental ethical principles and regulatory policies concerning AI systems require certain adaptations when it comes to the application of AI technology in connection with the sustainable development goals. The development of artificial intelligence systems compatible with the goals of sustainable development, as defined in the 1987 report of the UN Brundtland Commission as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”, is encouraged. Sustainability or sustainable development is defined in the literature as a concept based on three pillars - encompassing social, economic, and environmental aspects. The European Commission highlights that sustainable development is a fundamental principle of the Union Treaty and a priority goal of Union policies, along with digitization and a robust single market. In recent years, doctrine has sought to bridge the gap between the two disciplines by introducing the term “Sustainable AI”. The aim of the paper is to understand the development of AI that is compatible with sustainable goals. To understand this, it is necessary to comprehend the basic concepts of artificial intelligence systems and explore the sociological, ecological, and economic implications of these systems, all with the aim of finding ways to achieve the goals of sustainable development and the sustainability of AI systems themselves. These are closely tied to adhering to the highest ethical principles with the responsible use of artificial intelligence systems.

In medical practice, a situation requires legal and bioethical justification when a doctor decides on the choice of treatment tactics based on data that becomes available due to the development of artificial intelligence systems and which he is unable to verify himself with a reasonable investment of time, material and other resources. The field under consideration is fundamentally different from the field of verification and administrative procedures for recognizing medical devices that comply with technical regulations, since it does not eliminate the question of to what extent a doctor can legally rely on data from artificial intelligence systems, if, for example, they are counterintuitive. The involvement of artificial intelligence systems cannot be recognized as a trivial evolution of diagnostic technologies that exceed human capabilities, since there are questions about the rotation of expert opinions in the protocol of medical research, as well as the procedure for resolving contradictions. The authors present a study of the legal regulation of the use of artificial intelligence systems in the field of medical diagnostics, based on the analysis of regulatory material, scientific literature, own medical and legal practice. Based on an interdisciplinary approach, integrative solutions to problematic issues aimed at improving regulatory framework in a relatively new area of legal relations in the field of health are proposed. The authors consider the criteria for evaluating the effectiveness of medical solutions and the conditions for the onset of civil liability

The present article analyses the civil liability of legal advisors and barristers for damages resulting from the use of artificial intelligence (AI) systems in legal services. Taking into consideration the complexity of the issue, the study has been divided into three parts. In the first one, the concepts of artificial intelligence and AI systems are defined, key applications of AI in the practice of legal advisors and barristers are presented, and the basic aspects of professional liability for damages are discussed. In part two, detailed issues concerning the civil liability of legal advisors and barristers for damages caused to clients through the use of AI systems in the course of legal services are examined. Finally, part three investigates their liability towards certain third parties who are not clients (e.g. participants in legal proceedings) and explores the possibility of attributing liability to the AI system itself, while examining the extent to which such claims may be covered by insurance.

The present article analyses the civil liability of legal advisors and advocates for damages resulting from the use of artificial intelligence (AI) systems in legal services. Taking into consideration the complexity of the issue, the study has been divided into three parts. In the first one, the concepts of artificial intelligence and AI systems are defined, key applications of AI in the practice of legal advisors and advocates are presented, and the basic aspects of professional liability for damages are discussed. In part two, detailed issues concerning the civil liability of legal advisors and barristers for damages caused to clients through the use of AI systems in the course of legal services are examined. Finally, part three investigates their liability towards certain third parties who are not clients (e.g. participants in legal proceedings) and explores the possibility of attributing liability to the AI system itself, while examining the extent to which such claims may be covered by insurance. Keywords: artificial intelligence, LegalTech, civil liability, civil liability of legal advisors and advocates (attorneys-at-law), legal services.

Artificial intelligence has recently been intensively pushing the boundaries of human civilization and technological development. As a technological achievement, it imperceptibly "sneaked" into the life of every individual and became an integral part of all spheres of social life. In the application of artificial intelligence today, it seems that we cannot shake the impression that what at one point seemed possible in 100 years, has in fact become a reality in the last 10 or so years. The need for legal regulation and legal regulation of the use of artificial intelligence arises as an important issue for its future development for the benefit of the human community and the entire humanity. This research is part of a broader picture of the potential use of artificial intelligence in certain branches of law, aimed at looking at the special "interregnum" between the established practice of using artificial intelligence and its legal regulation. In the work, we focus in particular on the analysis and research of the use of artificial intelligence systems in the field of law enforcement. More precisely, we will shed light on the regulatory process related to artificial intelligence in the EU and point out the specifics of its use for security and police purposes, primarily by establishing restrictions for the benefit of the public and citizens, by categorizing the use of certain artificial intelligence systems as prohibited and high-risk. These are artificial intelligence systems that use prediction techniques, biometric surveillance, i.e. face recognition and derived biometric data. Their impact on human rights is the subject of special in-depth research outside this framework, bearing in mind that the uncontrolled use of these techniques as part of the artificial intelligence system today can be equated with the consequences of dropping an "atomic bomb" on human freedoms and rights.Apart from the regulatory process within the EU, we also point out the potential use of artificial intelligence in Serbia by law enforcement services through the analysis of the legal framework of the Republic of Serbia.

The relevance of this research is determined by the dualistic nature of the implementation of artificial intelligence systems in criminal proceedings. On the one hand, automated collection and analysis of big data creates new risks for information privacy and individual rights. On the other hand, artificial intelligence technologies themselves possess significant potential for enhancing data protection through predictive threat analysis, access control, and transparency. This paradox requires careful scientific understanding. The subject of this research is the legal, organizational, and technical mechanisms for ensuring data security when using artificial intelligence systems in criminal proceedings. The aim of the study is to demonstrate, through a comparative analysis of international experience (Germany and China), that appropriate legal regulation can transform the use of artificial intelligence systems from a source of threats into an effective tool for ensuring data security in criminal proceedings. The article employs a combination of general scientific and specialized methods. The basis for this research is a comparative legal analysis of criminal procedural regulation and AI application practices in Germany and China. The formal legal method was also used to analyze regulatory acts, and the system analysis method was used to study the architecture of AI systems as an element of procedural safeguards. The scientific novelty lies in the substantiation of the thesis that data security is ensured not by prohibiting or limiting the use of modern technologies, but by their procedural integration. Using the German model as an example, it is demonstrated that consistent legal regulation (the principle of targeted restriction, access gradation, and encryption requirements) creates the basis for the legitimate use of specialized artificial intelligence systems. The Chinese model of legal regulation also ensures data security, but not through strict prohibitions and restrictions, but through the centralized architecture of the AI system used in criminal proceedings. It is concluded that many risks associated with the use of AI systems are transitional in nature and can be overcome through legal regulation and technological development. Proposals have been formulated to improve current legislation: establishing the status of electronic data, “technologically neutral” encryption requirements, establishing criteria for admitting non-governmental developers, and obligations to ensure the interpretability of artificial intelligence solutions.

This chapter examines the use of artificial intelligence (AI) systems in the context of refugee forecasting and screening at the border. Refugees have a right to asylum under international law, which offers a framework for fair treatment and non-refoulement of refugees. AI systems offer efficiency and capacity management, and AI has been widely adopted for predictive analytics, credibility tests, scoring and ranking algorithms, and migration management. However, deploying AI systems raises ethical and legal concerns in terms of inaccuracy, bias, and a lack of transparency or interpretability affecting refugee rights. The current lack of holistic regulation around the use of AI systems deployed to perform state functions perpetuates the harms caused by this deployment. To address these shortcomings, the chapter suggests policy reforms aimed at reducing harms faced by refugees emanating from the use of AI systems. The chapter also puts forward institutional policy proposals to address issues of inaccuracy and interpretability.

Environmental problems have a tremendous impact on the entire world population, particularly on human health, which plays a leading role in individual well-being. Environmental pollution, according to some estimates, kills approximately 9 million people every year. The introduction of artificial intelligence (AI) systems in many areas has enormous potential in reducing human impact on the environment; however, such systems have negative effects. The potential of AI systems to improve healthcare is inextricably linked to the ethical challenges posed by the complexity of these systems and their impact on the lives and health of communities, patients, and staff. In addition to aspects that relate directly to the algorithms, data, and clinical application of AI systems, long-term risks exist that are not obvious at first glance. One of these risks is the negative impact of AI systems on the environment, which may harm human health indirectly. AI systems are more than software, having physical components that are necessary for their functioning, such as processors, memory, and sensors. The manufacture and the energy consumption of the components has a profound effect on the environment. One study showed that when a single AI algorithm is trained, carbon emissions may reach values corresponding to the total carbon emissions from five cars lifetime.
 This study analyzes existing literature linking the development of AI systems, especially in healthcare, to their effects on the environment. The study is intended to complement the emerging AI Ethics Code for healthcare, specifically the principles of sustainability that will be included in this code.
 The study concludes that the environmental impact of AI systems should be considered when formulating ethical standards for AI in healthcare. These standards must be considered during the development, testing, and application phases of AI systems. All the people involved in the creation and use of AI systems (developers, physicians, and regulators) must monitor the environmental impact and minimize the environmental consequences of such systems at all stages of their existence. This principle calls for minimizing negative impacts, improving the energy efficiency, and disposing physical components in strict compliance with current legislation. Moreover, the rapid development of AI systems and the ethical dilemmas require that solutions be proposed jointly and ethical standards be developed in a manner that is consistent and sensitive to emerging technologies.

The article examines the legal aspects of ensuring the patient’s right to informed voluntary consent in the provision of psychiatric care using artificial intelligence (AI) systems. Overall, the use of AI opens new possibilities for the diagnosis and treatment of mental disorders, offering significant potential to enhance the effectiveness of psychiatric care. However, the application of these technologies introduces various risks for patients, particularly concerning the protection of autonomy, the transparency of AI algorithms, and the security of personal data. Patients with mental disorders represent a particularly vulnerable group requiring additional legal guarantees in decision-making regarding treatment, especially when innovative technologies are involved. Based on an analysis of existing technologies, the authors identify a number of risks associated with the use of AI systems in psychiatric care, including: 1) violations of personal data confidentiality; 2) risks associated with decisions made by AI systems; 3) potential discrimination based on gender, race, religion, or other characteristics; 4) misuse in medical practice through the use of AI; 5) risks arising from malfunctions in AI systems; 6) other potential hazards. To mitigate these risks, the article considers legal regulatory measures, including the introduction of European legislation such as the AI Act, certification implementation, and the establishment of effective mechanisms for informed voluntary consent to AI use in psychiatry, given the high risks posed by this technology. The authors note that Ukrainian legislation currently lacks adequate mechanisms for obtaining informed consent in the use of AI for psychiatric care. The article proposes improvements to Ukrainian regulatory acts through the development of a separate consent form for the use of AI systems in psychiatric assessment or treatment, which would help to avoid the legal risks inherent in AI systems. Such a consent form would include detailed information for the patient about the specific AI systems to be used, their nature, purpose, and estimated duration of use. It would also inform the patient that the data collected and processed by the AI system would be protected according to data protection legislation, and it would include a verbal explanation of risks by the physician, as well as the options for choosing alternative treatment methods based on the doctor’s recommendations. The conclusions emphasize the importance of advancing national legislation to align with the AI Development Concept and international certification standards. This will ensure the protection of patients’ rights and foster the effective integration of AI in the field of psychiatric care.

The increasing use of Artificial Intelligence (AI) systems in face recognition and video processing in recent times creates higher stakes for their application in daily life. Increasingly, critical decisions are being made using these AI systems in application domains such as employment, finance, and crime prevention. These applications are done through the use of more abstract concepts such as emotions, trait evaluations (e.g., trustworthiness), and behavior (e.g., deception). These abstract concepts are learned by the AI system using the verbal and non-verbal cues from the human subject stimuli (e,g., facial expressions, movements, audio, text) for inference. Because the use of AI systems often happens in high stakes scenarios, it is of utmost importance that the AI system participating in the decision-making process is highly reliable and credible. In this paper, we specifically consider the feasibility of using such an AI system for deception detection. We examine if deception can be caught using multimodal aspects such as facial expressions and movements, audio cues, video cues, etc. We experiment using three different datasets with varying degrees of deception to explore the problem of deception detection. We also study state-of-the-art deception detection systems and investigate whether we can extend their algorithm into new datasets. We conclude that there is a lack of reasonable evidence that AI-based deception detection is generalizable over different scenarios of lying (lying deliberately, lying under duress, and lying through half-truths) and that in the future additional factors will need to be considered to make such a claim.

LAW IN DIGITAL REALITY

Objective: to explore the modern condition of the artificial intelligence technology in forming prognostic ethical-legal models of the society interactions with the end-to-end technology under study.Methods: the key research method is modeling. Besides, comparative, abstract-logic and historical methods of scientific cognition were applied.Results: four ethical-legal models of the society interactions with the artificial intelligence technology were formulated: the tool (based on using an artificial intelligence system by a human), the xenophobia (based on competition between a human and an artificial intelligence system), the empathy (based on empathy and co-adaptation of a human and an artificial intelligence system), and the tolerance (based on mutual exploitation and cooperation between a human and artificial intelligence systems) models. Historical and technical prerequisites for such models formation are presented. Scenarios of the legislator reaction on using this technology are described, such as the need for selective regulation, rejection of regulation, or a full-scale intervention into the technological economy sector. The models are compared by the criteria of implementation conditions, advantages, disadvantages, character of “human – artificial intelligence system” relations, probable legal effects and the need for regulation or rejection of regulation in the sector.Scientific novelty: the work provides assessment of the existing opinions and approaches, published in the scientific literature and mass media, analyzes the technical solutions and problems occurring in the recent past and present. Theoretical conclusions are confirmed by references to applied situations of public or legal significance. The work uses interdisciplinary approach, combining legal, ethical and technical constituents, which, in the author’s opinion, are criteria for any modern socio-humanitarian researches of the artificial intelligence technologies.Practical significance: the artificial intelligence phenomenon is associated with the fourth industrial revolution; hence, this digital technology must be researched in a multi-aspectual and interdisciplinary way. The approaches elaborated in the article can be used for further technical developments of intellectual systems, improvements of branch legislation (for example, civil and labor), and for forming and modifying ethical codes in the sphere of development, introduction and use of artificial intelligence systems in various situations.

Real-time cancer therapy success is what determines customising medicines, enhancing patient results, and lowering of unwanted side effects. This monitoring shapes the evolution of cancer therapy. The use of artificial intelligence (AI) in this field might result in a clear shift in the course of evolution of objects. This allows one to continuously and precisely monitor patient reaction to treatment. The purpose of this project is research on the many uses of artificial intelligence systems in the area of cancer therapy. More particularly, it underlines how they may combine imaging, genomic, and clinical records—among other types of data—to provide real-time information on the efficacy of these treatments. Artificial intelligence driven models—such as convolutional neural networks (CNNs) for image processing and natural language processing (NLP) for unorganised clinical data—allow one to find signals and patterns otherwise difficultly detected. These systems utilise adaptable algorithms able to learn from long-term data on people to project patient responses to treatments like immunotherapy, chemotherapy, and targeted drugs. This is made feasible by these systems' capacity to generate these predictions. Apart from improving the quality and speed of the information, the integration of intelligent devices collecting real-time biological data helps as well. We are highlighting how important it is for the many artificial intelligence technologies to cooperate, be scalable, and be used in a way fit for the architecture we have suggested. Real-time feedback loops enable oncologists to remain current with the most recent data, therefore enabling early intervention and customised treatment plan change. Case studies have shown how effectively these algorithms spot early indicators of treatment resistance and simplify the selection of which drugs to modify. Making use of real-time artificial intelligence systems presents some difficulties. Among these issues are concerns about data privacy, government rules must be strong, and unequal access to new technology in the healthcare industry. Resolving these problems will assist to ensure that cancer monitoring powered by artificial intelligence operates well and is deployed everywhere.

Patent law is increasingly challenged by the growing dissemination and use of artificial intelligence (AI) systems. Issues such as the patentability of AI systems or the consequences of the use of AI for the definition of the person skilled in the art and the inventive step have been discussed in the literature, and first decisions in different jurisdictions concerned the patentability of inventions developed by AI systems and the related question of a requirement of a human inventor. In contrast, the following contribution focuses on a field that has received little attention so far, namely patent infringement by development and use of AI systems, specifically artificial neural networks. After a brief introduction (see Sect. 1) and overview (see Sect. 2), different approaches to define artificial intelligence in the context of patent law will be presented (see Sect. 3) before possible acts of infringement are considered in detail (see Sect. 4) and the attribution of ‘autonomous’ acts of infringement by AI systems (see Sect. 5) and the question of fault (see Sect. 6) are discussed. A final conclusion (see Sect. 7) summarises the results.