THE INTERSECTION BETWEEN ARTIFICIAL INTELLIGENCE AND SUSTAINABILITY: CHALLENGES AND OPPORTUNITIES

This chapter explores relationships between the science and profession of psychology and the development of artificial intelligence (AI) and robotic systems. It briefly examines the role of psychology in the development of AI systems and theories. There is a long and intimate history of such a relationship, exemplified in the arts and the social sciences as well as in computer science and engineering. The chapter examines the role that psychology has played in the understanding of the ways in which humans interact with contemporary AI systems, to influence the behaviour of the systems and, in turn, to be influenced by them. Human decision making has been shown to be improved by factors other than intelligence and thereby appears to be a quality which would be retained by humans rather than machines, for example motivational processes, emotions and experience. The chapter examines the state of the profession of psychology and helping professions in the light of developments in AI.

LAW IN DIGITAL REALITY

"Background: Artificial Intelligence (AI) has become a ubiquitous phenomenon in recent times, with most organizations today attempting to maneuver their way around developing AI systems with the aim of improving the products and services they provide. However, what complicates developing AI systems is the paucity in frameworks to support organizations with AI System Development (SD). As a result, many organizations are using existing approaches which have been previously applied in earlier emerging Information Technology endeavors. This study explored how a framework can promote effective organizational AI SD. To achieve this a holistic framework for AI SD was conceptualized and examined from an organizational perspective. Method: This study has examined the conceptualized framework People-Process-Data-Technology (2PDT) in AI, through a case study research design. The empirical data was analyzed based on 12 case studies within Australia including 39 interviews with AI experts. We have applied thematic analysis to investigate requirements of organizational AI SD. Results: The results demonstrate that organizations are challenged by key factors, which inhibits their ability to effectively develop AI systems. For example, organizations are not achieving successful delivery of AI systems due to a lack of required skills. Additionally, a plethora of AI technology which is constantly evolving, poor data quality, and the paucity of AI SD frameworks are all contributing to unpredictable delivery outcomes. Conclusion: This paper investigated requirements for effective organizational AI SD by examining the 2PDT framework. The results contribute to AI phenomenon by developing the requirements for AI SD, in terms of people, process, data and technology. It contributes to theory by evaluating and developing AI requirements for effective AI systems. The examination of the framework and case study approach added valuable knowledge to the AI domain. In addition, we contributed to practice by identifying requirements that organizations should consider in achieving better AI SD outcomes."

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.

While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management.

Artificial intelligence technologies are developing intensively today, including due to the development of technologies of stable neural networks and cloud computing infrastructures, fuzzy system technologies, entropy administration, swarm intelligence, evolutionary computations, and many others. etc. At the same time, the problem of almost complete absence of normative legal regulation and normative technical regulation of the foundations, conditions and features of development, functioning and activities, integration into other systems and control over the use of artificial intelligence technologies is global today. The Article is devoted to the study of the specifics of the legal regulation of the use and development of artificial intelligence. Some approaches to the definition of artificial intelligence and the features of legal regulation of this sphere, which take place in the scientific literature, are considered and the author's definition of artificial intelligence is developed and given through the disclosure of its main features. In particular, according to the proposed definition, artificial intelligence is an artificial complex cybernetic computer-software-hardware system possessing the properties of substantivity, autonomy, and also the ability to perceive and analyze data, to self-learn. The question of positioning the artificial intelligence system as a special form of person (for example, the so-called «electronic person»), that is, granting it a certain legal personality depending on various factors and the field of application of such a system, is considered in this article. The article also notes the main possible approaches to legal support for the use and development of artificial intelligence systems, in particular, such approaches include the preemptive universal-total legal regulation and legal regulation aimed at the regulation of specific artificial intelligence systems. The main risks and uncertainties associated with artificial intelligence (and which are of great importance for the adoption of legislation in this field) have been investigated. There are drawn the conclusions about how to develop legislation on the use and development of artificial intelligence: consistently, taking into account the specifics of areas of its application, as well as balancing the interests of individuals, society and the state with regard to proper security and protection of individual rights, and interests related to the development of innovation for the benefit of the whole society.

The article is devoted to the study of the phenomenon of rapid transformation of the world, which is artificial intelligence (AI). It is noted that the aforementioned is due to the ability of AI to accelerate innovation, change economic processes, influence the ways in which people interact with technology, and open up new prospects for the development of humanity. It is noted that in recent years, unprecedented rates of AI implementation have been observed in key areas of life, which allows for significant changes in the labor market, scientific research, social communications, global security and the economy. Attention is focused on the successful implementation of AI in the military sphere, which in the current conditions of an undeclared war against the Russian Federation is extremely important, since this helps to properly ensure national security. Examples of the use of AI in various spheres of life in Ukraine are given, including public management, administration, and control, which helps to perform complex tasks and make decisions based on data analysis, and such use of AI will become increasingly widespread and significant. The integration of AI into public administration, in particular, will help to analyze regulatory and legal acts and translate European legislation. It is very important that digital expertise of regulatory and legal acts will be carried out thanks to AI tools. And in the future, of course, all these solutions can be scaled for the work of the government as a whole. Digital technologies, especially AI, are the future of digital governments. Ukraine has already established itself as a powerful player in the field of technology, and is now taking other important steps in the development of artificial intelligence. At the same time, attention is drawn to the fact that, along with the above-mentioned positive aspects of AI, concerns have been expressed in the world about the existence of a danger of the introduction of AI, the emergence of a potentially catastrophic risk for humanity. This is confirmed by the signing of the Bletchley declaration (November 1–2, 2023, Great Britain) on the first day of the Artificial Intelligence Security Summit organized by the British government, according to which countries agreed to work together on AI security research. It was emphasized that the summit participants focused on the need for appropriate international efforts to study and address the potential impact of AI systems, as well as recognizing that the protection of human rights, transparency and clarity, fairness, accountability, regulation, safety, appropriate public oversight, ethics, bias mitigation, privacy and data protection require urgent attention. It is noted that the above requires appropriate knowledge and consideration of advanced European experience in this area and an appropriate vision of AI. For this purpose, we will analyze the content of the scientific article by Oriol Mir Puigpel “The Impact of the AI Law on Public Authorities and Administrative Procedures”. The said Law (Regulation) will have an impact on automated decision-making by public authorities throughout Europe. In addition, Member States may supplement its provisions with their national acts on administrative procedure, which will positively affect the creation of a regulatory framework and the use of artificial intelligence systems by European public authorities. It is noted that the Law aims to guarantee the free movement of artificial intelligence systems with equal application to entities of different forms of ownership. In this case, public authorities are usually considered “users”. At the same time, they will also be “suppliers” if they develop their own AI systems themselves or purchase an individual AI system. The Law prohibits certain AI systems and imposes numerous obligations on suppliers and, to a lesser extent, on users of high-risk systems, and defines four levels of risk for AI systems. The Law’s impact on EU and Member State public authorities when using or developing AI systems is disclosed, and their respective responsibilities are also defined.

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

The availability of big data at universities enables the use of artificial intelligence (AI) systems in almost all areas of the institution: from administration to research, to learning and teaching, the use of AI systems is seen as having great potential. One promising area is academic performance prediction (APP), which is expected to provide individual feedback for students, improve their academic performance and ultimately increase graduation rates. However, using an APP system also entails certain risks of discrimination against individual groups of students. Thus, the fairness perceptions of affected students come into focus. To take a closer look at these perceptions, this chapter develops a framework of the “perceived fairness” of an ideal-typical APP system, which asks critical questions about input, throughput and output, and based on the four-dimensional concept of organizational justice, sheds light on potential (un-)fairness perceptions from the students' point of view.

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

The article discusses the main trends in the development of artificial intelligence systems and robotics (AI&R). The main question that is considered in this context is whether artificial systems are going to become more and more anthropomorphic, both intellectually and physically. In the article, the author analyzes the current state and prospects of technological development of artificial intelligence and robotics and also determines the main aspects of the impact of these technologies on society and economy, indicating the geopolitical strategic nature of this influence. The author considers various approaches to the definition of artificial intelligence and robotics, focusing on the subject-oriented and functional ones. In the article, AI&R abilities and human abilities are compared in such areas as categorization, pattern recognition, planning and decision making, etc. Based on this comparison, it is concluded in which areas AI&R’s performance is inferior to a human and in which cases it is superior to one. The modern achievements in the field of robotics and artificial intelligence create the necessary basis for further discussion of the applicability of goal setting in engineering, in the form of a Turing test. It is shown that development of AI&R is associated with certain contradictions that impede the application of Turing’s methodology in its usual format. The basic contradictions in the development of AI&R technologies imply that there is to be a transition to a post-Turing methodology for assessing engineering implementations of AI&R. In such implementations, on the one hand, the “Turing wall” is removed, and on the other hand, artificial intelligence gets its physical implementation.

As the hostilities in Ukrainian cities remind us once again, urban warfare persistently causes immense suffering and devastating consequences for civilians’ lives and livelihoods. At the same time, the events reveal the challenges urban warfare represents for militaries. The urban environment is one of the most complex environments within which to conduct military operations. To address the challenges this environment poses to militaries, several technologically advanced States are investing in the development of artificial intelligence to enhance a range of their military activities. The ways States have thus far prioritized the development of artificial intelligence systems, however, evidence that investments to improve militaries’ ability to mitigate civilian harm during urban warfare remain rather neglected. Hence, this article aims to demonstrate that despite the risks related to artificial intelligence applications, this technology has great potential for enhancing militaries’ ability to mitigate civilian harm further. But this requires governments to invest more in its development and use to that end. Recognizing artificial intelligence systems’ potential to reduce the military challenge of protecting civilians from harm during urban warfare is a pressing need, considering that conflicts increasingly occur in urban environments where the risks for civilians’ lives and their livelihoods grow exponentially.

Green construction procurement is a part of the sustainable development goals (SDGs) that influence economic growth on a strategic level. Adopting green technologies and practices has no longer been an option but a well-worth route for gaining a competitive advantage in the construction sector. The emergent concepts of green procurement and sustainability have raised the need to measure the financial performances in supply chain practices. Green procurement is now gaining importance in the construction industry and supply chain practices for a safer tomorrow. The study aimed to investigate the impact of construction procurement on green logistic services, innovation practices, and their subsequent role in gaining sustainable economic development goals. The population frame for this study was the project teams (engineers and project managers) of construction companies in China. The sampling design followed was convenient random sampling. The study was conducted using self-administered questionnaire surveys to avoid any respondent biases and/or errors. The study's findings show that construction procurement is partially associated with green logistic services management, which thoroughly determines the sustainable economic development goals. Furthermore, construction procurement is found to have a significant impact on green innovation practices that partially predict the SDGs, and the mediation of green logistic services and innovation practices are partially mediated to the construction procurement and sustainable goals. In the future, the study results will help the construction practitioners, contractors, bidders, and engineering community to shape their relationships in a greener way up or down the stream of their supply chains. Additionally, the implication of green procurement and logistic services offers many challenges in the long run for attaining SDGs; however, in the short run, it gives operational efficiency and less hazardous environmental emissions.

The paper discusses the use of artificial intelligence technology in legal activities. The author examines the problem of differentiating between weak and strong artificial intelligence. Attention is focused on the possibility of creating a superintelligence in the future, which already creates the need for scientists to pay close attention to the issues of introducing a system of ethical values into artificial intelligence. The paper highlights the problems of creating datasets necessary for training neural networks intended for the use in legal practice, as well as their quality. The author analyzes specific examples of the use of artificial intelligence systems in domestic and foreign legal practice. The author also justifies the possibility of considering the use of artificial intelligence as a separate type of legal technology. It is concluded that despite the large number of developments successfully used in legal practice based on artificial intelligence systems, they are all examples of weak artificial intelligence and act only as a means of increasing the efficiency of a lawyer, but do not replace it. The future of legal activities will be associated with an effective combination of artificial intelligence functionality and creative expert professional skills of a lawyer.

Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review