Use Of Artificial Intelligence Systems Research Articles

Trustworthy Artificial Intelligence in Education: Pitfalls and Pathways

Across the world, Artificial Intelligence (AI) applications are entering all domains of our lives, including the educational environment. This development was further spurred by the COVID-19 pandemic, which rendered many educational institutions dependent on (AI-enabled) digital learning tools to continue their activities. While the use of AI systems can lead to numerous benefits, it can also entail ethical risks, which are increasingly appearing on legislators’ agendas. Many of these risks are context-specific and increase in significance when vulnerable individuals are involved, asymmetries of power exist, or human rights and democratic values are at stake more generally. Surprisingly, however, regulators have thus far paid only little attention to the specific risks arising in the context of Artificial Intelligence in education (AIED). This paper hence aims to assess the ethical challenges posed by AIED. Its normative framework consists of the seven requirements for Trustworthy AI, as set out in the Ethics Guidelines of the European Commission’s High-Level Expert Group on AI. After an overview of the broader context in which these requirements took shape (Section 2), the paper examines each requirement in the educational realm, as well as the pitfalls that should be addressed to enable their realisation (Section 3). Particular attention is given to the special role of education in shaping people’s minds, and the manner in which this role can be used both to empower and exploit individuals. The paper notes that AIED’s main strengths – offering education on a wider scale, through more flexible and individualised learning methods, and through the closer monitoring of students’ reception of the materials – are also its main liabilities when left unchecked. Finally, the paper discusses various pathways that policymakers should consider to foster Trustworthy AIED beyond the adoption of guidelines (Section 4), before concluding (Section 5).

Population health AI researchers' perceptions of the public portrayal of AI: A pilot study.

This article reports how 18 UK and Canadian population health artificial intelligence researchers in Higher Education Institutions perceive the use of artificial intelligence systems in their research, and how this compares with their perceptions about the media portrayal of artificial intelligence systems. This is triangulated with a small scoping analysis of how UK and Canadian news articles portray artificial intelligence systems associated with health research and care. Interviewees had concerns about what they perceived as sensationalist reporting of artificial intelligence systems – a finding reflected in the media analysis. In line with Pickersgill’s concept of ‘epistemic modesty’, they considered artificial intelligence systems better perceived as non-exceptionalist methodological tools that were uncertain and unexciting. Adopting ‘epistemic modesty’ was sometimes hindered by stakeholders to whom the research is disseminated, who may be less interested in hearing about the uncertainties of scientific practice, having implications on both research and policy.

Use of machine learning in geriatric clinical care for chronic diseases: a systematic literature review.

ObjectivesGeriatric clinical care is a multidisciplinary assessment designed to evaluate older patients’ (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases.Materials and MethodsWe restricted our search to eight databases, namely PubMed, WorldCat, MEDLINE, ProQuest, ScienceDirect, SpringerLink, Wiley, and ERIC, to analyze research articles published in English between January 2010 and June 2019. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions.ResultsWe identified 35 eligible studies and classified in three groups: psychological disorder (n = 22), eye diseases (n = 6), and others (n = 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications.ConclusionMore studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.

ВИКОРИСТАННЯ СИСТЕМ ШТУЧНОГО ІНТЕЛЕКТУ У ЗАДАЧАХ ПРОГНОЗУВАННЯ ФІНАНСОВИХ ІНДЕКСІВ: ОГЛЯД НАУКОВИХ ДЖЕРЕЛ

The paper investigates methods of artificial intelligence in the prognostication and analysis of financial data time series. The usage of well-known methods of artificial intelligence in forecasting and analysis of time series is investigated. Financial time series are inherently highly dispersed, complex, dynamic, nonlinear, nonparametric, and chaotic nature, so large-scale and soft data mining techniques should be used to predict future values. As the scientific literature superficially describes the numerous artificial intelligence algorithms to be used in forecasting financial time series, a detailed analysis of the relevant scientific literature was conducted in scientometric databases Scopus, Science Direct, Google Scholar, IEEExplore, and Springer. It is revealed that the existing scientific publications do not contain a comprehensive analysis of literature sources devoted to the use of artificial intelligence methods in forecasting stock indices. Besides, the analyzed works, which are related in detail to the object of our study, have a limited scope because they focus on only one family of artificial intelligence algorithms, namely artificial neural networks. It was found that the analysis of the use of artificial intelligence systems should be based on two well-known approaches to predicting the behavior of financial markets: fundamental and technical analysis. The first approach is based on the study of economic factors that have a possible impact on market dynamics and more common in long-term planning. Representatives of technical analysis, on the other hand, argue that the price already contains all the fundamental factors that affect it. In this regard, technical analysis involves forecasting the dynamics of price changes based on the analysis of their change in the past, ie time series. Although today there are many developed models for forecasting stock indices using artificial intelligence algorithms, in the scientific literature there is no established methodology that defines the main elements and stages of the algorithm for forecasting financial time series. Therefore, this study has improved the methodology for forecasting financial time series.

Prospects for the use of artificial intelligence systems in gastroenterology

The lecture is devoted to a new direction in clinical medicine — the possibility of using artificial intelligence — the field of computer science, which is engaged in modeling the method of acquiring and using knowledge specific to humans. The basis for a correct diagnosis is a combination of experience, the ability to think and act non-standard in difficult cases. A powerful system of generalization and classification, implemented in intelligent systems, allows you to reduce a huge number of possible situations to a small number of typical situations by which decisions or hypotheses are formed.

Systematization and improvement legal regulation of the use of artificial intelligence in law enforcement

The article systematizes legal acts at the international, Federal and departmental levels on the use of artificial intelligence in law enforcement.
 In particular, at the international level, the corresponding legal act, according to the author, should contain three components of legal regulation: 1) regulate the organization and construction of an artificial intelligence system between States; 2) determine the principles of functioning of artificial intelligence; 3) regulate the ethical issues of using artificial intelligence.
 The legal basis for the use of artificial intelligence systems in law enforcement at the Federal level, in addition to the Constitution of the Russian Federation, should be the Strategy for the development of the information society in the Russian Federation (Strategy) for 2017-2030, as well as some special Federal laws (for example, the Law of Moscow), which are designed to specify the mechanism for using the advantages of artificial intelligence in all spheres of public life, including law enforcement.
 The author notes that the mentioned strategy defines only the General provisions of the state policy on the development of information technologies and artificial intelligence technologies. At the same time, the implementation of national interests outlined in the Strategy is impossible without the effective work of law enforcement agencies, whose functioning, in turn, is impossible without a well-built system of interaction between information support and artificial intelligence.
 The final part of the paper presents some proposals for improving the legal regulation of the use of artificial intelligence in law enforcement, in particular, justifies the need to adopt a national Doctrine for the use of a Unified system of information support and artificial intelligence in the activities of law enforcement agencies.

Algorithm of software package of intellectual decision support when designing cyber security system at the enterprise

Introduction . To increase the decision-making efficiency at the enterprise, it is advisable to use a special software package of intellectual support. Such a product is necessary when designing an information security system and increasing its invulnerability during modernization or configuration changes. Research objectives are as follows: to develop an algorithm and a mathematical model of the software package for intellectual decision support. Materials and Methods . The decision support method under designing an information security system is based on the use of a neural network (multilayer perceptron). For an objective assessment of the initial security of an information system (IS), a mathematical model for the analysis of security events is developed. Results . The statistics of malicious attacks on the IS of enterprises is analyzed. The need for timely and accurate modernization of the information protection system is determined. Important characteristics of the designing an information security system are the speed at which the result is obtained and the reduction in the residual risk of IS. In this regard, the use of artificial intelligence systems in the process of determining the best set of protection subsystems is important. The threats to cyber security (CS) are classified. The main classes of security events are defined. A mathematical model of the neural network is developed; the input parameters of its operation are indicated. The current enterprise IS generates numerous events which necessitates the automatic collection and analysis of data from subsystems for registering IS objects. The process of analyzing security events is considered in detail since the adequacy of the generated design decisions depends on the correctness of the data obtained in this way. The algorithm of the software package is formed. Discussion and Conclusions . The results can be used in the design of the information security system at the enterprise. In addition, CS administrators can use the developed software package to adjust the configuration settings of information security tools. The proposed solution will minimize the destructive influence of the developer of the security system which may and happen to be subjective.

Application of modern technologies in the field of crime prevention

The opinion that the processes of digitalization and informatization take place in all spheres of social life is expressed. A striking feature of the modern digital society is the widespread use of technologies. They are an important factor in improving the effectiveness of law enforcement. Technologies, when used by of-fenders, are simultaneously transformed into a circumstance that complicates the detection and investigation of crimes. CCTV (closed-circuit television) technology is now widely used in crime prevention. It is also called the city video surveillance system. In recent years, it has been significantly upgraded by artificial intelligence. It allows you to process a large amount of data and automatically identify certain objects without human intervention. China is considered to be the leader in the number of video surveillance cameras and the use of artificial intelligence systems. A significant number of video cameras also take place in the United States, Ger-many, Great Britain, and Japan. The technological trend of crime prevention in the leading countries of the world is the use of robotics. The UAE invests the significant funds in these technologies. In this state, in the coming years, every fourth policeman will be a robot. To use drones is a common practice for police in many countries. Crime mapping also contributes to improving the efficiency of the police. This technology is used to predict local crime and individual criminal behaviour. Special attention should be paid to biotechnologies that are related to the re-searches in the field of genetics. Although a specific "criminal gene" has not yet been detected, scientists have made a breakthrough in explaining the biological causes of criminal behaviour of certain types of offenders. A number of suggestions for the implementation of modern technologies in law enforcement in Ukraine are provided.

PROBLEMS OF THE FORMATION OF THE CONCEPTUAL APPARATUS IN THE FIELD OF ARTIFICIAL INTELLIGENCE

The study of the problems of the formation of the conceptual ap para tus in the field of legal support of artificial intelligence to develop effective le gal solutions in order to regulate new digital technologies. The work is based on a set of general scientific and special legal methods, including analysis, description, ge ne ra li zation, com parative law. The formation of legal definitions of artificial intelligence and related concepts (ro bot, cyber-physical system, etc.) requires the identification of the main legal fe atures of artificial intelligence. The following key characteristics of artificial in tel li gence are identified: optional hardware implementation; the ability of the system to analyze the environment; autonomy in operation; the ability to accumulate experience, its assessment and implementation of the task of self-learning; the pre sence of "intelligence", described through the categories of "rationality", "rationality" or simply the ability to "think like a person" or "act like a person" in all or in narrowly defined circumstances. Future legal regimes of artificial intelligence should take into account the possibility of weak artificial intelligence as a kind of complex thing and strong artificial intelligence that will "require" new legal solutions. The formation of legal definitions in the field of artificial intelligence must meet the requirements of universality, system city and ethics, and also allow, based on the selected properties, to establish systemic hierarchical relationships between concepts used in the field of artificial intelligence. The study develops the theoretical and practice-oriented provisions of in for mation law in matters of legal support for artificial intelligence, and also creates the basis for the formation of legislation in this area. The work can be used in further research activities on the legal regulation of the development, functioning and use of artificial intelligence systems.

RESPONSABILIDADE CIVIL E REGULAÇÃO DE NOVAS TECNOLOGIAS: QUESTÕES ACERCA DA UTILIZAÇÃO DE INTELIGÊNCIA ARTIFICIAL NA TOMADA DE DECISÕES EMPRESARIAIS

RESUMO: O presente artigo tem por objeto central a discussão acerca do regime de responsabilidade civil dos agentes na tomada de decisões baseadas na utilização de sistemas de inteligência artificial. A partir de um panorama acerca da regulação da inteligência artificial no Brasil, passa-se a uma abordagem sintética sobre as principais teorias da responsabilidade civil aplicáveis, utilizando-se como caso para análise a conduta dos agentes empresariais que, no âmbito das sociedades empresárias, delegam decisões, total ou parcialmente, para sistemas automatizados, cujo processo algorítmico nem sempre possui uma explicação clara, mas que, não raro, conduz a danos. Nesse cenário, as normas constitucionais, o dever de diligência e padrões éticos se mostram como parâmetros relevantes para a aferição da responsabilidade civil desses agentes.

Social Technologies and Principles of Criminal Justice in the Context of its Digitalization

The paper analyzes the problems that arise in the process of digitalization of criminal proceedings, when its organizers are forced to organize the interaction of social and computer technologies, the joint development and application of which become inevitable in modern conditions. The basic concepts of social technologies theory that were originally developed as a means of organizing human activities, with criminal justice system being only one of them, are interpreted in a new way. The main contexts of application of "social technology" concept, as well as the significance of social technologies in the formulation of principles and achieving the goals of criminal proceedings are studied. The author defines a special type of social technologies, humanitarian technologies in the paper, and analyzes the relevance of this type of technology in the system of principles of criminal procedure. The author substantiates the need to distinguish between the theoretical and technological aspects of compliance with the principles of criminal justice, which allows us to reconstruct the entire system of goal-setting impact on people’s actions and social relations through the development and application of social technologies. The concept of gradual transformation of society through the use of social engineering, put forward by K. Popper, is analyzed in detail. The role of the theoretical distinction between methodological essentialism and methodological nominalism (antirealism) is reconstructed. The ideological and political contextualization of the theory of social technologies, which dominates in modern social science, is subjected to critical analysis. The author examines the ontological assumptions and methodological guidelines proposed by K. Popper for the approval of his socio-technological doctrine. The author identifies and describes the specifics of the use of social technologies in the context of the use of artificial intelligence systems in criminal proceedings. The point about the need to search for the relationship and mutual consistency of the systems of the individual and society in the process of digitalization of criminal proceedings both at the level of principles and at the level of technology is substantiated.

Interdependência Securitária: o Caso da Proliferação e Controlo de Armamentos no Domínio da Inteligência Artificial

This article examines the more obscure dimensions of the use of Artificial Intelligence systems in Defense, with a particular focus on lethal autonomous weapon systems. Based on the need to regulate these disruptive technologies in military applications, this paper defends the preventive prohibition of these armaments and makes proposals for a global regulation of the use of Artificial Intelligence in military strategy. The article argues that autonomous systems aggravate the difficulties in managing the instruments of armed violence, which may undermine the foundations of strategy. It also defends the need to promote a global arms control architecture, taking into account that today it is already possible to use Artificial Intelligence applications in all military operational domains and that these are increasingly interrelated.

Ethics of autonomous weapons systems and its applicability to any AI systems

Most artificial intelligence technologies are dual-use. They are incorporated into both peaceful civilian applications and military weapons systems. Most of the existing codes of conduct and ethical principles on artificial intelligence address the former while largely ignoring the latter. But when these technologies are used to power systems specifically designed to cause harm, the question must be asked as to whether the ethics applied to military autonomous systems should also be taken into account for all artificial intelligence technologies susceptible of being used for those purposes. However, while a freeze in investigations is neither possible nor desirable, neither is the maintenance of the current status quo. Comparison between general-purpose ethical codes and military ones concludes that most ethical principles apply to human use of artificial intelligence systems as long as two characteristics are met: that the way algorithms work is understood and that humans retain enough control. In this way, human agency is fully preserved and moral responsibility is retained independently of the potential dual-use of artificial intelligence technology.

The Use of Artificial Intelligence Systems in Law Enforcement

The paper examines the technological basis and opportunities for the use of artificial intelligence systems in law enforcement. The authors describe the investigation methods and the essence of artificial intelligence, and conduct a detailed study of approaches to the taxonomy of its systems. Artificial intelligence today does not only make it possible to solve specific tasks, but also approaches human cognition in its functions. In the present legal environment, programming and automation of crime investigation and solving are used to create information and reference systems, as well as databases and criminalistic algorithms that optimize, for example, the process of developing and verifying criminalistic leads, planning an investigation, supporting the maintenance of order, searching for the culprit, etc. The authors define key features of artificial neural networks viewed as one of the main methods of using artificial intelligence systems in law enforcement, specifically, situational adaptive learning, ability to identify non-obvious links and regularities. The designing of an applied artificial neural network is examined stage-by-stage. At the first stage, a dataset is collected - it is a volume of data for training the network. At the second stage, an algorithm (a set of rules) for learning is selected or designed. After that comes the process of learning and validating its results. The authors analyze the criteria for evaluating the effectiveness of training an artificial intelligence system, including the criteria of precision and accuracy. They single out three key types of operations in the sphere of law enforcement that can be performed by artificial intelligence systems: identification (of visual images and links between the objects of criminalistic study), prediction and classification.

INTELIGENCIA ARTIFICIAL Y JUSTICIA PENAL: MÁS ALLÁ DE LOS RESULTADOS LESIVOS CAUSADOS POR ROBOTS

El presente trabajo aborda las implicaciones del uso de sistemas de Inteligencia Artificial en la justicia penal. Más allá de los resultados lesivos causados por máquinas, se analiza primero qué es la IA, cuáles son sus características y tipologías y sus usos en relación con la justicia criminal y cuál se prevé que sea su evolución. Se analizan después los principales riesgos éticos que plantea con especial interés por lo que supone para la privacidad y por la denominada «discriminación algorítmica».This paper addresses the implications of the use of Artificial Intelligence systems in criminal justice. Beyond the harmful results caused by machines, we first analyse what AI is, what their characteristics and typologies are and their uses regarding criminal justice, and what its evolution is expected to be. We then analyse with special interest the main ethical risks it poses concerning privacy and the so-called «algorithmic discrimination».

Innovative business-models of logistics support of enterprise development

Purpose. The aim of the article is generalization of scientific-theoretical and improvement of methodological approaches to substantiation of innovative business- model of logistical support of innovative development of the enterprise for timely satisfaction of consumer demand. Methodology of research. General scientific and special methods are used in the process of research, in particular: system analysis - to determine the impact of external and internal environment, market infrastructure on innovative business models of logistics of enterprise development; abstract and logical - in determining the possibilities of logistical support for the development of the enterprise and meeting the needs of consumers. Findings. The need for enterprise development in the form of an efficient and competitive logistics system, which is currently costly and imperfect, negatively affects the investment attractiveness and competitiveness of products, enterprises and the country. It is established that logistics belongs to the category where the impact of digitalization is difficult to fully assess and implement due to the need for comprehensive modernization of infrastructure and technological processes. The sphere of logistics services in Industry 4.0 is characterized by features that are both indicators of the development of digitalization: the use of big data and cloud technologies; the spread of the Internet of Things; robotics development; dissemination of 3D printing technology; block-chain; crowdsourcing. Originality. The logistical support of the innovative business model of modern enterprise development is proposed, which should be based on a system set of methods and organizational and management mechanisms of information technologies, software packages to achieve strategic development goals based on innovative transformation and implementation and use of artificial intelligence systems and Smart-grid technologies, network structures, clusters or hubs in search of opportunities to strengthen innovation capacity to meet the needs of consumers in a timely manner. Practical value. The obtained results of the study can be used to expand the introduction of logistics innovations as part of innovative development of the enterprise, achieve better results of enterprises of industrial and non-industrial spheres, which will bring them to a new level of development and improve the economic situation from the standpoint of logistics as time requirements are constant monitoring of the market situation for the use of new methods of work and constant modifications of products or services. Key words: innovations, logistics, technologies, products, modification, development, enterprise, attractiveness, commercialization, efficiency, consumers.

Расширение применения систем искусственного интеллекта в банковской сфере

Расширение применения систем искусственного интеллекта в банковской сфере

A “psychopathic” Artificial Intelligence: the possible risks of a deviating AI in Education

AbstractThis work analyses the use of artificial intelligence in education from an interdisciplinary point of view. New studies demonstrated that an AI can “deviate” and become potentially malicious, due to programmers’ biases, corrupted feeds or purposeful actions. Knowing the pervasive use of artificial intelligence systems, including in the educational environment, it seemed necessary to investigate when and how an AI in education could deviate. We started with an investigation of AI and the risks it poses, wondering if they could be applied also to educative AI. We then reviewed the increasing literature that deals with the use of technology in the classroom, and the criticism about it, referring to specific use cases. Finally, as a result, the authors formulate questions and suggestions for further research, to bridge conceptual gaps underlined by lack of research.

Artificial intelligence in medicine-the wrong track or promise of cure?

Artificial intelligence (AI) has attained a new level of maturity in recent years and is developing into the driver of digitalization in all areas of life. AI is a cross-sectional technology with great importance for all branches of medicine employing imaging as well as text and biodata. There is no field of medicine that remains unaffected by AI, with AI-assisted clinical decision-making assuming a particularly important role. AI methods are becoming established in medial workflow management and for prediction of therapeutic success or treatment outcome. AI systems are already able to lend support to imaging-based diagnosis and patient management, but cannot suggest critical decisions. The corresponding preventive or therapeutic measures can be more rationally assessed with the help of AI, although the number of diseases covered is currently far too low for the creation of robust systems for clinical routine. Prerequisite for the comprehensive use of AI systems is appropriate training to enable physicians to decide when computer-assisted decision-making can be relied upon.

Perseptron for assessing the students’ task performance in learning a foreign language according to the competencies using bi data

The article presents the Perceptron neural network program developed by the authors, which enables assessing in points the quality of the task performed by students in a foreign language. The theoretical basis for the artificial intelligence systems applied in the learning process has been considered. The studies have shown that the online education market has grown in the world, and the trend marked persists. The trends in use of neural networks in learning have been summarized. Analysts noted that the use of artificial intelligence systems has been becoming more widely used in the learning process. It has been hypothesized and proved that a neural network makes it possible to give a grade for the quality of a control text read by a student according to the requirements of competencies using the BI data resulted from processing with an artificial intelligence system. The algorithm proposed by the authors uses the capabilities of the PyPhox mobile application and the Deductor platform. The algorithm is based on the following scheme. The teacher records a piece of a text into a file on the PhyPhox mobile application; its digitized version is then used as a reference to compare the quality of the assignment done by a Master student. The resulting reference file is exported to XL and then imported into the Deductor for further processing. The students do the same; their control files are paired with the reference, combined in XL and imported into the Deductor for processing. In the final, based on the comparison of the coefficients of correlation between the control and reference files, the Perceptron program assigns a score corresponding to the “excellent,” “good,” “satisfactory” and “not satisfactory” grades. The neural network formed is trained on data sampling, where they add semester points and competence points. The relevance lies in the fact that the program enables automating the learning process and replacing the teacher’s work at the stage of the assignment control according to the competencies, which is of practical importance.