Correction: Reciprocal trust and distrust in artificial intelligence systems: the hard problem of regulation

The possibility of neuro-linguistic textual identification of intelligent systems (IS) and artificial intelligence (AI) systems is investigated. To set the task, intelligent systems were assigned a task in two languages, and artificial intelligence systems were assigned in four languages, using AI systems of different generations. As part of the study, a specialized software package was used to evaluate information parameters, as well as an information analyzer designed for neuro-linguistic identification of texts. The results make it possible to use information characteristics as parameters of neuro-linguistic identification of artificial intelligence (AI) and intelligent systems (IS) systems. The results of the study showed that during the transition from one system to another, the parameters of neuro-linguistic text identification change both in the study of intelligent systems and in the study of artificial intelligence systems. In the study of AI systems, the parameters of neuro-linguistic text identification change when switching from one language to another in one neural network, when changing neural networks while maintaining the same language. In the study of intelligent systems, it was revealed that the parameters change during the transition from one language to another, when changing the intelligent system while maintaining one language. This study makes it possible to use information characteristics as parameters of neuro-linguistic textual identification of artificial intelligence systems and intelligent systems.

The article analyzes the influence of artificial intelligence on various spheres of social life: educational, cultural, economic, and others, from the standpoint of understanding the role of a person and setting their thinking under the influence of artificial intelligence systems. The influence of artificial intelligence on the spheres of human life raises the inevitable question of the transformation of the legal norms governing these areas. No matter how technologically advanced artificial intelligence systems are, there is always a risk of making an incorrect decision, which can be associated with causing damage to property, harm to life and health. An important and conceptual problematic areas of regulation is the development of a model of responsibility for causing harm using artificial intelligence and robotics systems. Approaches to identifying the culprit are considered in the article. Several situations stand out when deciding the issue of responsibility for causing harm when using artificial intelligence systems. Firstly, situations of deliberate distortion of program code at the stage of creating or training an artificial intelligence system are possible. In such a situation, of course, the responsibility falls on the person involved in the creation or training. Given that, as a rule, several persons are involved in such a process, difficulties may arise with the identification of a specific guilty subject, similarly to the situation when a violent crime is committed by a group of persons and a fatal blow is inflicted by one of the members of the group. Secondly, situations are possible when harm is caused due to the illegal seizure of control of the artificial intelligence system. In such a situation, responsibility also lies with the attacker - a physical person. Thirdly, there may be situations when, in the process of self-learning, artificial intelligence systems come to certain conclusions that do not depend on the actions of developers, and, as a result, harm is caused. Disputes about the responsibility of such artificial intelligence just arise in the event of the autonomy of its decision, that is, regardless of human actions. It is concluded that at this stage our society is not ready for the recognition of artificial intelligence as an independent subject capable of bearing responsibility, especially when it comes to the criminal legal sphere. To resolve the issue of the possibility of recognition (or non-recognition) of artificial intelligence as an independent entity of responsibility, it is necessary to proceed from the general provisions of the theory of legal responsibility, correlating its constituent elements with the characteristics of artificial intelligence systems, which requires independent comprehension and research. The authors declare no conflicts of interests.

Artificial intelligence to complement rather than replace radiologists in breast screening

As Artificial Intelligence (AI) systems become more widespread, there is a growing need for transparency to ensure human understanding and oversight. This is where Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations is still an open research problem. Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. Essential techniques include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods while Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. To ensure that explanations are tailored for diverse users, contexts, and AI applications, HCI principles and participatory design approaches can be utilized. Therefore, this article concludes with recommendations for developing human-centred XAI systems, which can be achieved through interdisciplinary collaboration between HCI and AI. As Artificial Intelligence (AI) systems become more common in our daily lives, the need for transparency in these systems is becoming increasingly important. Ensuring that humans clearly understand how AI systems work and can oversee their functioning is crucial. This is where the concept of Explainable AI (XAI) comes in to make AI systems more transparent and interpretable. However, developing adequate explanations for AI systems is still an open research problem. In this context, Human-Computer Interaction (HCI) is significant in designing interfaces for explainable AI. By integrating HCI principles, we can create systems humans understand and operate more efficiently. This article reviews the HCI techniques that can be used for solvable AI systems. The literature was explored with a focus on papers at the intersection of HCI and XAI. The essential methods identified include interactive visualizations, natural language explanations, conversational agents, mixed-initiative systems, and model introspection methods. Each of these techniques has unique advantages and can be used to provide explanations for different types of AI systems. While Explainable AI presents opportunities to improve system transparency, it also comes with risks, especially if the explanations need to be designed carefully. There is a risk of oversimplification, leading to misunderstanding or mistrust of the AI system. It is essential to employ HCI principles and participatory design approaches to ensure that explanations are tailored for diverse users, contexts, and AI applications. By developing human-centred XAI systems, we can ensure that AI systems are transparent, interpretable, and trustworthy. This can be achieved through interdisciplinary collaboration between HCI and AI. The recommendations in this article provide a starting point for designing such systems. In essence, XAI presents a significant opportunity to improve the transparency of AI systems, but it requires careful design and implementation to be effective.

Introduction: when studying legal issues related to safety and adequacy in the application of artificial intelligence systems (AIS), it is impossible not to raise the subject of liability accompanying the use of AIS. In this paper we focus on the study of the civil law aspects of liability for harm caused by artificial intelligence and robotic systems. Technological progress necessitates revision of many legislative mechanisms in such a way as to maintain and encourage further development of innovative industries while ensuring safety in the application of artificial intelligence. It is essential not only to respond to the challenges of the moment but also to look forward and develop new rules based on short-term forecasts. There is no longer any reason to claim categorically that the rules governing the institute of legal responsibility will definitely not require fundamental changes, contrary to earlier belief. This is due to the growing autonomy of AIS and the expansion of the range of their possible applications. Artificial intelligence is routinely employed in creative industries, decision-making in different fields of human activity, unmanned transportation, etc. However, there remain unresolved major issues concerning the parties liable in the case of infliction of harm by AIS, the viability of applying no-fault liability mechanisms, the appropriate levels of regulation of such relations; and discussions over these issues are far from being over. Purpose: basing on an analysis of theoretical concepts and legislation in both Russia and other countries, to develop a vision of civil law regulation and tort liability in cases when artificial intelligence is used. Methods: empirical methods of comparison, description, interpretation; theoretical methods of formal and dialectical logic; special scientific methods: legal-dogmatic and the method of interpretation of legal norms. Results: there is considerable debate over the responsibilities of AIS owners and users. In many countries, codes of ethics for artificial intelligence are accepted. However, what is required is legal regulation, for instance, considering an AIS as a source of increased danger; in the absence of relevant legal standards, it is reasonable to use a tort liability mechanism based on analogy of the law. Standardization in this area (standardization of databases, software, infrastructure, etc.) is also important – for identifying the AIS developers and operators to be held accountable; violation of standardization requirements may also be a ground for holding them liable under civil law. There appear new dimensions added to the classic legal notions such as the subject of harm, object of harm, and the party that has inflicted the harm, used with regard to both contractual and non-contractual liability. Conclusions: the research has shown that legislation of different countries currently provides soft regulation with regard to liability for harm caused by AIS. However, it is time to gradually move from the development of strategies to practical steps toward the creation of effective mechanisms aimed at minimizing the risks of harm without any persons held liable. Since the process of developing AIS involves many participants with an independent legal status (data supplier, developer, manufacturer, programmer, designer, user), it is rather difficult to establish the liable party in case something goes wrong, and many factors must be taken into account. Regarding infliction of harm to third parties, it seems logical and reasonable to treat an AIS as a source of increased danger; and in the absence of relevant legal regulations, it would be reasonable to use a tort liability mechanism by analogy of the law. The model of contractual liability requires the development of common approaches to defining the product and the consequences of violation of the terms of the contract.

In many applications, decision making has recently become dependent on artificial intelligence (AI) systems. In order to ensure a safe integration of such systems within these applications, not only should their accuracy and performance be tested, but also their trustworthiness. We discuss here the basic phases involved when testing the trustworthiness of an AI system, as well as some of the steps that can be taken to ensure an AI system is trustworthy. We begin by discussing characteristics which should be considered for most AI systems, prior to moving on to other characteristics of trustworthiness which can be essential for some AI systems, particularly those which are sensitive and have a direct impacton people’s lives. We also shed light on the fact that trustworthiness, along with its evaluation, should be fit for purpose and should be aligned with the original context in which the respective AI system will be deployed. We also examine the role of third-party testing in the development and deployment of AI and ML systems, outlining some of the related benefits, risks, and best practices for mitigating these risks.

This scoping study explores the role of Human-in-the-Loop Artificial Intelligence (HITL AI) in precision agriculture and evaluates the benefits of using human expertise in combination with Artificial Intelligence (AI) systems in decision-making within modern smart agricultural environments. The development of Artificial Intelligence, Machine Learning, Internet of Things, and robotics has significantly impacted modern agriculture by providing automated crop monitoring, disease detection, yield prediction, and smart farm management systems. However, Artificial Intelligence systems also face challenges in terms of understanding, interpretability, flexibility, and trustworthiness in modern smart agricultural environments. This study is based on the literature regarding human-in-the-loop systems, human-centric Artificial Intelligence systems, and collaborative robotics systems in the context of smart agriculture. The structured scoping study methodology has been followed to identify and evaluate studies regarding Artificial Intelligence systems in smart agricultural environments, with a focus on automation-centric Artificial Intelligence systems and human-centric Artificial Intelligence systems within the context of Agriculture 5.0 concepts. The study concludes that although automation-centric AI systems show high accuracy in simulated smart agricultural environments, Human-In-The-Loop (HITL) AI systems show higher robustness in smart agricultural environments. Explainability in AI has shown significant potential in supporting the effectiveness of HITL AI systems in decision-making within smart agricultural environments. The study also identifies some important gaps in the literature regarding HITL AI systems in smart agriculture. The study concludes that for the development of modern smart precision agriculture, collaborative intelligence within smart agricultural environments is necessary to create sustainable smart agriculture systems.

Is the future of medical diagnosis in computer algorithms?

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.

Generating Rembrandt: Artificial Intelligence, Accountability and Copyright - The Human-Like Workers Are Already Here - A New Model

The question of expediency and the principal possibility of machine imitation of human intellect from the point of view of evaluating the perspectives of various directions of development of artificial intelligence systems is discussed. It is shown that even beyond this practical aspect, the solution to the question about the principal possibility of creating a machine equivalent of the human mind is of great importance for understanding the nature of human thinking, consciousness and mental in general. It is noted that the accumulated experience of creating various systems of artificial intelligence, as well as the currently available results of studies of human intelligence and human consciousness in philosophy and psychology allow us to give a preliminary assessment of the prospects of creating an algorithmic artificial system, equal in its capabilities to human intelligence. The analysis of the drawbacks revealed in the use of artificial intelligence systems by mass users and in scientific research is carried out. The key disadvantages of artificial intelligence systems are the inability to independently set goals, the inability to form a consolidated «opinion» when working with divergent data, the inability to objectively evaluate the results obtained and generate revolutionary new ideas and approaches. The disadvantages of the «second level» are the insufficiency of information accumulated by mankind for further training of artificial intelligence systems, the resulting training of models on the content partially synthesized by artificial intelligence systems themselves, which leads to «forgetting» part of the information obtained during training and increasing the cases of issuing unreliable information. This, in turn, makes it necessary to check the reliability of each answer given by the artificial intelligence system whenever critical information is processed, which, against the background of the plausibility of the data given by artificial intelligence systems and a comfortable form of their presentation, requires the user to have well-developed critical thinking. It is concluded that the main advantage of artificial intelligence systems is that they can significantly increase the efficiency of information retrieval and primary processing, especially when dealing with large data sets. The importance of the ethical component in artificial intelligence and the creation of a regulatory framework that introduces responsibility for the harm that may be caused by the use of artificial intelligence systems is substantiated, especially for multimodal artificial intelligence systems. The conclusion is made that the risks associated with the use of multimodal artificial intelligence systems consistently increase in the case of realization in them of such functions of human consciousness as will, emotions and following moral principles.

As automation is changing everything in today’s world, there is an urgent need for artificial intelligence, the basic component of today’s automation and innovation to have standards for software engineering for analysis and design before it is synthesized to avoid disaster. Artificial intelligence software can make development costs and time easier for programmers. There is a probability that society may reject artificial intelligence unless a trustworthy standard in software engineering is created to make them safe. For society to have more confidence in artificial intelligence applications or systems, researchers and practitioners in computing industry need to work not only on the cross-section of artificial intelligence and software engineering, but also on software theory that can serve as a universal framework for software development, most especially in artificial intelligence systems. This paper seeks to(a) encourage the development of standards in artificial intelligence that will immensely contribute to the development of software engineering industry considering the fact that artificial intelligence is one of the leading technologies driving innovation worldwide (b) Propose the need for professional bodies from philosophy, law, medicine, engineering, government, international community (such as NATO, UN), and science and technology bodies to develop a standardized framework on how AI can work in the future that can guarantee safety to the public among others. These standards will boost public confidence and guarantee acceptance of artificial intelligence applications or systems by both the end-users and the general public.

A Novel Deep Learning–based Artificial Intelligence System for Interpreting Urolithiasis in Computed Tomography

The technological revolutionary achievements of the modern world inevitably pose a number of issues to humanity that require legal reflection. The most breakthrough achievements of the last few years are artificial intelligence systems. These systems are very successfully integrated into many spheres of life of the world community. To date, many countries have no systematic legal norms regulating the scope of artificial intelligence. The aim of this work is to formulate specific proposals for the legislative regulation of the field of artificial intelligence. To reach this aim, the authors analyzed legislative acts and law enforcement practice in the Russian Federation and in other modern technologically developed countries. Special attention was paid to determining the possibility of considering artificial intelligence as a subject of law. As part of the work, the authors also examined the doctrinal points of view of both the domestic and foreign scientific community. Based on the results of a comprehensive study, the authors propose to consider the possibility of attributing limited legal personality to some artificial intelligence systems: not all artificial intelligence systems should be given certain rights and responsibilities, but only those that have signs of strong artificial intelligence. In this regard, the authors propose to classify all artificial intelligence systems depending on how significant legal facts and legal consequences they are able to generate. It is advisable to structure the list of “advanced” artificial intelligence systems into one group – “strong intelligent systems”. The list of less developed artificial intelligence systems should be included in another group – “weak intelligent systems”. It is advisable classify artificial intelligence systems not on the principle of a generalized enumeration of their functionality, but on the principle of their specific literal enumeration. A specific list of “strong intelligent systems” will be formed and approved by the Government of the Russian Federation. Further, in connection with the proposed classification, comes the idea of attributing legal personality to “strong intelligent systems”. By analogy with the institution of legal entities, it is possible to provide a procedure for delegating certain rights and obligations to strong artificial intelligence, thereby bringing out a new subject in certain legal relations. Thus, the results of the study can outline certain boundaries of the work of artificial intelligence, contributing to the creation of specific constituent documents or protocols of functioning.

This study was aimed to evaluate two artificial intelligence (AI) systems that created a prioritized problem list and treatment plan, and examine whether the performance of the aforementioned systems was equivalent to orthodontists. A total of 967 consecutive cases [800: training; 67: validation; 100: evaluation (40: randomly selected for the clinical evaluation)] were used. We used a stored document that describes (1) the patient's clinical information, (2) the prioritized list, and (3) a treatment strategy without digital tooth movement. Sentences of (1) were vectorized according to the bag of words method (V); sentences of (2) and (3) were relabelled with 423 and 330 labels, respectively. AI systems that output labels for the prioritized list (subtask 1) and treatment planning (subtask 2) based on the vectors V were developed using a support vector machine and self-attention network, respectively, while the system was trained to improve precision and recall. Clinical evaluations were conducted by four orthodontists (no faculty or residents; peer group) in two sessions: in the first session, peer group and the developed AI systems created problem lists and treatment plans; in the second session, two of the peer group (not AI) evaluated these lists and plans, including the lists and plans of the AIs, by scoring them using 4-point scales [unacceptable (1) to ideal (4)]. Scores were compared among the system and peer group (Wilcoxon signed-rank test, P < 0.05). The precision after system training was 65% and 48% for subtasks 1 and 2 respectively, with recall of 55% and 48%, respectively. The clinical evaluation of the AI system for subtask 1 showed a mid-rank. For subtask 2, the AI system had a significantly lower score than the three panels but the same rank with one panel. Two AI systems that output a prioritized problem list and create a treatment plan were developed. The clinical system ability of the former system showed a mid-rank in the peer group, and the latter system was almost equivalent to the worst orthodontist.