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Research on the issues and problems of the introduction of artificial intelligence into various spheres of life of a modern individual, the society, and the state are in the trend of scientific research at the present stage of information technology development. Artificial intelligence (AI) is being used now more than ever before, especially in the healthcare sector. Artificial intelligence has provided a more efficient way to automate routine work and other daily tasks, as well as manage patients and medical resources. The system can perform most of the tasks previously performed by humans, making it faster and cheaper. This significant advantage has facilitated the activities of interacting parties in the healthcare sector, doctors and patients. Artificial intelligence continues to gain momentum. Currently, there are modern machine learning solutions that can act, learn, understand, and predict. This is a step forward compared to robotic assistants in surgical interventions and the binding of genetic codes previously controlled by artificial intelligence. The article demonstrates that the development of artificial intelligence in healthcare involves some risks and problems — artificial intelligence systems expose patients to the risk of injury, and patient data for use in artificial intelligence systems involve the risk of privacy invasion. The article shows the advantages and disadvantages of the introduction of artificial intelligence in the field of healthcare, and the necessary conclusions are drawn.

Artificial intelligence (AI) is increasingly reshaping service by performing various tasks, constituting a major source of innovation, yet threatening human jobs. We develop a theory of AI job replacement to address this double-edged impact. The theory specifies four intelligences required for service tasks—mechanical, analytical, intuitive, and empathetic—and lays out the way firms should decide between humans and machines for accomplishing those tasks. AI is developing in a predictable order, with mechanical mostly preceding analytical, analytical mostly preceding intuitive, and intuitive mostly preceding empathetic intelligence. The theory asserts that AI job replacement occurs fundamentally at the task level, rather than the job level, and for "lower" (easier for AI) intelligence tasks first. AI first replaces some of a service job's tasks, a transition stage seen as augmentation, and then progresses to replace human labor entirely when it has the ability to take over all of a job's tasks. The progression of AI task replacement from lower to higher intelligences results in predictable shifts over time in the relative importance of the intelligences for service employees. An important implication from our theory is that analytical skills will become less important, as AI takes over more analytical tasks, giving the "softer" intuitive and empathetic skills even more importance for service employees. Eventually, AI will be capable of performing even the intuitive and empathetic tasks, which enables innovative ways of human–machine integration for providing service but also results in a fundamental threat for human employment.

Artificial intelligence systems are currently deployed in many areas of human activity. Such systems are increasingly assigned tasks that involve taking decisions about people or predicting future behaviours. These decisions are commonly regarded as fairer and more objective than those taken by humans, as AI systems are thought to be resistant to such influences as emotions or subjective beliefs. In reality, using such a system does not guarantee either objectivity or fairness. This article describes the phenomenon of bias in AI systems and the role of humans in creating it. 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1. Introduction -- 2. What is Artificial Intelligence? -- 2.1. Definitions -- 2.2. History -- 2.3. State of play and future prospects -- 3. Bioethical inquiries about artificial intelligence -- 3.1. Bioethical issues common to weak and strong artificial intelligence -- 3.2. Bioethical issues resulting from strong artificial intelligence -- 3.2.1. Ontological discussions -- 3.2.2. Consequential discussions -- 4. Medicine and artificial intelligence -- 4.1. Use of artificial in health services -- 4.2. Main challenges in medical ethics -- 4.2.1. Confidentiality and privacy -- 4.2.2. Compassion, veracity and fidelity -- 4.2.3. Communication skills and case based approach -- 5. Conclusion.

Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes.

"'Artificial Intelligence in Highway Safety' is a cutting-edge book to the hurtling world of AI in the field of highway safety. The author Subasish Das, a highway safety expert, pursues highway safety within its contexts, while drawing attention to the predictive powers of the AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. This book is suitable for anyone interested in highway safety and AI and provides an illuminating and accessible introduction to this fast-growing research trend. The book's website at http://subasish.github.io/AIiHS offers a variety of supplemental materials, including data sets and R codes. 'Artificial Intelligence in Highway Safety' is a cutting-edge book to the hurtling world of AI in the field of highway safety. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples"--

Artificial Intelligence in Highway Safety provides cutting-edge advances in highway safety using AI. The author is a highway safety expert. He pursues highway safety within its contexts, while drawing attention to the predictive powers of AI techniques in solving complex problems for safety improvement. This book provides both theoretical and practical aspects of highway safety. Each chapter contains theory and its contexts in plain language with several real-life examples. It is suitable for anyone interested in highway safety and AI and it provides an illuminating and accessible introduction to this fast-growing research trend. Material supplementing the book can be found at https://github.com/subasish/AI_in_HighwaySafety. It offers a variety of supplemental materials, including data sets and R codes

Abstract
For their deployment in human societies to be safe, AI agents need to be aligned with value-laden cooperative human life. One way of solving this "problem of value alignment" is to build moral machines. I argue that the goal of building moral machines aims at the wrong kind of ideal, and that instead, we need an approach to value alignment that takes seriously the categorically different cognitive and moral capabilities between human and AI agents, a condition I call deep agential diversity. Domestication is the answer to a similarly structured problem: namely, how to integrate nonhuman animals that lack moral agency safely into human society and align their behavior with human values. Just like nonhuman animals, AI agents lack a genuinely moral agency; and just like nonhuman animals, we might find ways to train them to nevertheless assist us, and live and work among us – to "domesticate" them, in other words. I claim that the domestication approach does well in explaining many of our intuitions and worries about deploying AI agents in our social practices.