Use Of Artificial Intelligence Algorithms Research Articles

Problems of protection of labor rights during hiring with the use of artificial intelligence algorithms

Abstract In recent years, artificial intelligence has found wide application in labor law, including in hiring processes. Artificial intelligence algorithms are used to automate recruiting, skills assessment and decision making. Although this can provide certain advantages and efficiency in the selection of candidates, the use of artificial intelligence algorithms in hiring also creates new legal problems and challenges, in particular in the context of the protection of labor rights in hiring: discrimination, transparency of artificial intelligence algorithms, protection of personal data. The problems caused by the use of artificial intelligence in labor law create challenges for lawyers in the context of creating ethical criteria and legal frameworks that regulate the use of artificial intelligence in the hiring process. The purpose of this article is to outline the main legal issues related to the violation and protection of labor rights in the case of the use of artificial intelligence algorithms in hiring. To achieve the goal of the research, methods of analysis, generalization, formal-logical, comparison, forecasting, dialectical and others were used. The current state of Ukrainian legislation and the experience of foreign countries are considered. The signs by which the artificial intelligence system can be classified as high-risk are highlighted. The problems of personal data protection during recruitment using artificial intelligence algorithms are analyzed. The definition of discrimination contained in international legal acts has been studied. Insufficient legal regulation of discrimination with the use of artificial intelligence algorithms has been established, which in turn creates problems in law enforcement. The criteria necessary to prevent manifestations of discrimination during recruitment with the use of artificial intelligence algorithms are highlighted. On the basis of the conducted research, a conclusion was made about the insufficient legal regulation of the use of artificial intelligence algorithms in domestic legislation, criteria that should become key for the protection of labor rights during employment with the use of artificial intelligence algorithms.

AI applications in video games and future expectations

In recent years, artificial intelligence (AI) techniques have been extensively applied in various industries, including the gaming industry. The integration of different AI models into games has brought about a new era of game development, optimization, and overall gaming experience. This paper presents an overview of several AI algorithms that have been utilized in games, discussing their applications and providing predictions for the future of AI in gaming. It outlines how these algorithms have been incorporated into games to improve various aspects of gameplay. The application part of the paper focuses on game content optimization, specifically from the perspective of enhancing player experience. It examines areas such as player interaction and in-game elements, explaining how AI algorithms have been used to optimize these aspects and create a more immersive gaming experience. Furthermore, the future expectations section revolves around further advancements in optimizing the gaming experience and incorporating AI into the game design and development stages. It also discusses potential improvements in hardware that can better support AI integration in games. Finally, the article concludes with a summary and expectations for the future development of AI in gaming, highlighting the potential benefits and advancements that can be achieved through the continued use of AI algorithms.

Целостность и иерархичность структур данных в жизненном цикле объектов малоэтажного жилищного строительства

The subject area of the article is construction facilities life cycle management, where the object-oriented automated technologies of low-rise housing construction are the subject of consideration. The purpose of this study is to describe the meta-structure of such technologies, consisting of a set of hierarchical structures of the finite elements (premises, structures, engineering equipment, etc.) and for this meta-structure definition the concept of low-rise housing estate construction facilities integrity is introduced. The object of the article's research is hierarchical data structures, on the basis of which object-oriented databases and knowledge bases are formed, serving automated technologies for construction facilities integrity generation within all stages of the life cycle. The article uses the method of decomposition of the low-rise housing estate construction facilities integrity into a variety of automated technologies and their constituent hierarchical data structures, which allows us to identify all subject areas of objects of manor low-rise housing estate construction for computational justifications of the limiting states of finite elements (objects) of architecture, building structures, engineering equipment. Hierarchical structures create a standardized basis for the organization of finite element information models for managing the life cycle of manor low-rise estate housing construction. Decision-makers should prepare hierarchical data structures and algorithms for calculating the justifications for finite elements. The article presents examples of hierarchical data structures schemes for the design stage tasks solving. It is concluded that all information models of finite elements on a variety of subject areas of manor low-rise estate housing construction objects arise during the execution of constructive (algorithmized) processes in the decision-making layers of the corresponding stages of the life cycle of construction objects. It is concluded that all information models of finite elements on a variety of subject areas of manor low-rise estate housing construction objects arise during the execution of constructive (algorithmized) processes in the decision-making layers of the corresponding stages of the life cycle of construction objects. A structuring condition is also proposed: if the main link of the life cycle management technology is the information model of the object, then the fundamental condition for object orientation and meaningful filling of hierarchical data structures is the construction system used in this construction object, as a set of load-bearing and enclosing structures, its components and materials used. The proposed approach will make it possible to create and maintain a shared data environment on a regular and ongoing basis as part of a set of hierarchical data structures of the construction projects under consideration. In the development, it becomes possible to qualitatively improve the technological conditions for decision-making according to various criteria, including criteria for environmental friendliness, energy efficiency, economy, comfort for low-rise residential buildings using neural network data structures and the use of artificial intelligence algorithms. The above together is the basis for the formation of a modern innovative technological order for the objects of estate low-rise housing construction.

Predicting the Performance and Adaptation of Artificial Elbow Due to Effective Forces using Deep Learning

Measuring power transmission in organs poses a significant challenge for researchers in the field, with various methods being explored, including the use of artificial intelligence algorithms. This study focused on developing a new neural network model to predict force transmission and performance in an artificial elbow. Rather than evaluating natural joints, the study simulated a prosthetic model using medical software. Empirical data was collected using MIMICS software to estimate power properties and transmission methods, which were then used to train a neural network in MATLAB. The neural network demonstrated strong performance, particularly with the use of CNN architecture. The model's accuracy was validated by comparing results with experimental data from Anatomy and Physiology Comparison software, showing that the neural network provided precise results.

Evaluating the role of artificial intelligence in automated image analysis for x-ray radiography

This article intends to assess how artificial intelligence (AI) affects the automation of X-ray radiography image processing. The field of medical imaging has seen considerable potential in the use of AI algorithms to improve diagnostic precision, streamline procedures, and streamline workflow. The paper explores how AI is currently being used to automate image processing for X-ray radiography, outlining its possible benefits, difficulties, and hopes for the future. According to the results, AI has the potential to revolutionize radiography by helping radiologists evaluate images, locate anomalies, and do quantitative analysis. This discovery may result in important improvements in healthcare. To determine the clinical value and safety of AI-driven solutions, it is necessary to carry out more research and validation. In conclusion, this analysis highlights AI’s critical role in automating image processing for X-ray radiography and demonstrates how it has the potential to completely transform the industry. To guarantee the dependability and efficacy of AI-based techniques in clinical practice, however, continual research and validation are crucial.

Using artificial neural networks to predict indoor particulate matter and TVOC concentration in an office building: Model selection and method development

Indoor particulate matter and TVOC pose a significant threat to the health of staff working in office buildings. The accurate prediction of indoor particulate matter and TVOC concentrations is crucial for the automatic operation of ventilation and filtration system to control these indoor pollutants. Artificial neural networks have the advantages of high accuracy, real-time monitoring, and multi-source data integrations. Their performances in predicting particulate matter and TVOC concentrations in office buildings need to be further studied. In this study, on-site sampling was conducted in a typical office building at northern China. A standardized database of indoor parameters for four different offices was created with a total of 6072 datasets for each parameter. Three artificial neural network models were used in this study: the back propagation neural network (BP-ANN), the multi-layer neural network (MLNN), as well as the long-term and short-term memory neural network (LSTM). Among these three models, the performance of the MLNN model was the best in predicting PM2.5 and PM10 concentrations. It achieved a FB ranging from -0.02 to -0.01, an NMSE ranging from 0.46 to 0.49 μg/m3, and an R2 between 0.78 and 0.81. The MLNN model and the random forest (RF) classification method were further used to predict indoor TVOC concentrations. The RF model achieved a relatively better performance with a prediction accuracy of 89.2 %. In addition, the models’ generalization abilities were further evaluated by using some smaller datasets. For the MLNN model, when predicting indoor PM2.5 concentration, as the amount of training data decreased from 80 % to 20 %, its FB decreased from 0.41 to 0.03, its NMSE changed from 1.53 μg/m3 to 0.53 μg/m3, and its R2 decreased from 0.69 to 0.07. The results in this study can contribute to the use of artificial intelligence algorithm in office buildings aiming at indoor pollutants control.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Возможности и угрозы применения искусственного интеллекта (ИИ) в политических коммуникациях

The article examines aspects related to the opportunities and threats of using artificial intelligence (AI) in politi-cal communications. The conclusion is drawn about the dual nature of the use of artificial intelligence (AI) in political communications. Attention is drawn to the key current threats to the use of AI in political communica-tions – the spread of deepfakes and the use of AI algorithms in disinformation, manipulation of public con-sciousness or gaining advantages in the selective delivery of information to recipients. A comprehensive ar-gument is given for the depth of the problems associated with the potential use of AI in electoral communica-tions and the difficulties that arise in overcoming them. Practical measures are proposed to level out the nega-tive aspects of the potential use of artificial intelligence in political and, above all, electoral communications in the Russian Federation.

Застосування алгоритмів штучного інтелекту в глобальній енергетичній індустрії

Cyber security, energy conservation, minimization of electricity losses, fault diagnosis, and re-newable energy sources were analyzed. Specific engineering problems have been defined for each field of energy, for which the use of artificial intelligence algorithms has been analyzed. Research has shown that AI algorithms can improve the processes of energy production, distri-bution, storage, consumption and trading.

Ukrainian education for peace and security 2023: Technological convergence, artificial intelligence

The article highlights the impact of the integration of artificial intelligence (AI) into the educational environment, focusing on the possibilities of technological convergence. Based on the recognition of the changing world and the need to adapt educational approaches to the challenges of today, the study offers a deeper understanding of the role of innovative technologies. The study reveals the potential of AI to create personalised, adaptive learning paths for students of different educational and qualification levels. The use of AI algorithms to customise the learning process to individual needs promotes critical thinking and collaboration, which are important for building a safe social environment. The study highlights the possibility of using AI to reduce social inequality in areas affected by military aggression. The introduction of AI in the educational process can ensure equal access to quality education for all regions. The authors of the article emphasise the need for a multidisciplinary approach to the use of AI in education, where the interaction of educators and civil society is crucial for the implementation of technologies with ethics and efficiency. An innovative approach to education supported by AI can foster cooperation, understanding, and cohesion, contributing to a stable and harmonious society.

AI in supply chain optimization: A comparative review of USA and African Trends

The integration of Artificial Intelligence (AI) in supply chain management has emerged as a critical driver of efficiency and competitiveness in global markets. This paper provides a comparative review of AI trends in supply chain optimization between the United States and African regions, shedding light on the unique challenges and opportunities faced by each. In the United States, AI adoption in supply chain optimization has been robust, with a focus on predictive analytics, machine learning, and advanced automation technologies. American companies leverage AI to enhance demand forecasting, optimize inventory management, and streamline logistics processes. The integration of AI-driven solutions has allowed U.S. businesses to achieve higher accuracy in demand predictions, reduce lead times, and minimize operational costs. Furthermore, the use of AI algorithms in route optimization has significantly improved delivery efficiency, leading to enhanced customer satisfaction. Contrastingly, African countries are experiencing a more gradual but steadily increasing adoption of AI in supply chain optimization. Limited access to advanced technology infrastructure, coupled with resource constraints, has posed challenges for African businesses. However, innovative approaches are being explored, such as the use of mobile technologies and cloud-based solutions to overcome infrastructure limitations. AI applications in African supply chains focus on improving visibility, minimizing waste, and ensuring timely delivery. The continent's diverse supply chain landscape, encompassing agriculture, mining, and manufacturing, presents a unique set of challenges that AI aims to address. Both the United States and African nations recognize the potential of AI to transform supply chain management. While the U.S. is at the forefront of AI implementation, Africa is forging ahead with tailored solutions that align with its specific context. Collaboration and knowledge exchange between these regions could pave the way for a globalized approach to AI in supply chain optimization. This review underscores the importance of understanding regional nuances in adopting AI technologies, fostering collaboration for mutual benefit, and advancing the global evolution of AI-driven supply chain management.

Is the spread of excitation different between adults and children cochlear implants users?

While cochlea is adult size at birth, etiologies and bone density may differ between children and adults. Differences in neural response thresholds (tNRT) and the spread of excitation (SOE) width may impact the use of artificial intelligence algorithms in speech processor fitting. To identify whether neural response telemetry threshold and spread of excitation width are similar in adults and children. Retrospective cross-sectional study approved by the Ethical Board. Intraoperative tNRT and SOE recordings of consecutive cochlear implant surgeries in adults and children implanted with Cochlear devices (Cochlear™, Australia) were selected. SOE was recorded on electrode 11 (or adjacent, corresponding to the medial region of the cochlea) through the standard forward-masking technique in Custom Sound EP software, which provides SOE width in millimeters. Statistical comparison between adults and children was performed using the Mann-Whitney test (p ≤ 0.05). Of 1282 recordings of intraoperative evaluations, 414 measurements were selected from children and adults. Despite the tNRT being similar between adults and children, SOE width was significantly different, with lower values in children with perimodiolar arrays. Besides, it was observed that there is a difference in the electrode where the SOE function peak occurred, more frequently shifted to electrode 12 in adults implanted. In straight arrays, there was no difference in any of the parameters analyzed on electrode 11. Although eCAP thresholds are similar, SOE measurements differ between adults and children in perimodiolar electrodes.

Use of artificial neural networks in architecture: determining the architectural style of a building with a convolutional neural networks

AbstractThe discussion of "can machines think?" which started with the invention of the modern computer, brought along the question of "can machines design?" by researchers in the design field. These developments in information technologies have also affected the architecture. Artificial intelligence applications are encountered in many areas such as pricing estimation, energy conservation security systems of buildings, ventilation systems, user-oriented interactive design solutions, computer-aided programs used in the plan production phase and design process. When the literature on artificial intelligence applications in the architecture is reviewed, it can be seen that it generally includes shape grammars, graph theory, decision trees, constraint-based models, machine learning methods, RNN (Recursive Neural Networks), CNN (Convolutional Neural Network) and GAN (Generative Adversarial Network) algorithms. In this study, the use of artificial intelligence algorithms in architecture was examined, and an example was designed to determine the architectural structures of different periods by using CNN (Convolutional Neural Network). In the study, the open source TensorFlow library developed by Google and the Python programming language were used. Employing a statistical approach and utilizing convolutional neural networks (CNNs), a study has successfully classified the current flow patterns of buildings based on datasets comprising facades of Gothic, Modern, and Deconstructivist architectural styles. The findings demonstrate the efficacy of CNNs in accurately distinguishing the intricate details of diverse architectural styles. Recognizing elements from different periods using the CNN algorithm can examine not only individual buildings but also the relationship of buildings with their environments. It can also gain an important place in the field of conservation of the architectural discipline. The historical processes, aesthetic features and changes of protected buildings can be learned with the CNN algorithm and can guide restoration decisions. As a result of the study, the employed CNN-based model can correctly classify structures with 84.66% accuracy rate.

Investigation the generalized extreme value under liner distribution parameters for progressive type-Ⅱ censoring by using optimization algorithms

<abstract><p>Several random phenomena have been modeled by using extreme value distributions. Based on progressive type-Ⅱ censored data with three different distributions (i.e., fixed, discrete uniform, and binomial random removal), the statistical inference of the generalized extreme value distribution under liner normalization (GEVL distribution) parameters is investigated in this study. Since there is no analytical solution, determining the maximum likelihood parameters for the GEVL distribution is considered to be a problem. Standard numerical methods are frequently insufficient for this dilemma, requiring the use of artificial intelligence algorithms to address this difficulty. Here, nonlinear minimization and a genetic algorithm have been used to tackle that problem. In addition, Lindley approximation and Monte Carlo estimation were implemented via Metropolis-Hastings algorithms to carry out the Bayesian point estimation based on both the squared error loss function and LINEX loss functions. Moreover, the highest posterior density intervals were applied. The proposed theoretical inference techniques have been applied in a numerical simulation and a real-life example.</p></abstract>

The use of AI algorithms in architecture, engineering and construction: A tool for crisis prevention? The uncertainty perspective

Within the Architecture, engineering and construction (AEC) sector we see movements toward greater use of industrial robots, machine learning, algorithms, and other artificial intelligence (AI) tools. Yet, the AEC industry, despite being one of the largest fields on a global scale, is known for being the slowest to digitalize and innovate. Factors such as unrecognizing the value of digitalization by the decision-makers and making safety-related decisions under high levels of uncertainty, appear to be critical in preventing successful large-scale digitalization. This situation raises multiple questions from a risk science perspective. How, among other things, might the expansion of AI and more specifically AI algorithms usage in the AEC field affect uncertainties, and could AI be considered a tool for preventing crises? To obtain responses to these questions, we conducted 21 semi-structured, in-depth interviews with AEC employees who are currently using AI and AI algorithms or will soon be doing so in their everyday work. Our findings show potential for wider use within the AEC field, subject to overcoming knowledge gaps. Moreover, although having the potential to reduce some uncertainties, the increased use of AI and AI algorithms appears to be introducing an entirely new set of uncertainties. As a result, although AI may effectively prevent certain crises and be regarded as crisis prevention tool, its inadequate implementation could potentially create new risks.

Revolutionizing Drug Design with Artificial Intelligence: A Comprehensive Review of Techniques, Applications, and Case Studies

Introduction: Artificial intelligence (AI) has the potential to revolutionize drug design and discovery by significantly reducing the time and costs involved in developing new drugs. This literature review aims to explore the use of AI in drug design, focusing on virtual screening, de novo drug design, and prediction of ADME properties. Objective: The objective of this review is to provide an overview of the AI techniques used in drug design and their applications in virtual screening, de novo drug design, and prediction of ADME properties. The review also aims to summarize the advantages and limitations of these approaches and present case studies and examples showcasing their use in drug design. Methodology: A comprehensive search of academic databases was conducted, and 11 relevant articles were selected for inclusion in this review. The selected articles were analyzed to identify the AI techniques used in drug design, their applications, advantages, and limitations. Case studies and examples were also examined to demonstrate the efficacy of AI in drug design. Results: AI techniques such as machine learning, deep learning, and reinforcement learning have been successfully used in virtual screening, de novo drug design, and prediction of ADME properties. Virtual screening involves the use of AI algorithms to identify promising compounds for further testing, while de novo drug design involves the generation of novel compounds using AI techniques. Prediction of ADME properties involves the use of AI to predict the absorption, distribution, metabolism, and excretion of drug candidates. The case studies and examples presented in this review demonstrate the potential of AI to accelerate drug design and discovery. Conclusion: AI has the potential to revolutionize drug design and discovery by significantly reducing the time and costs involved in developing new drugs. Virtual screening, de novo drug design, and prediction of ADME properties are among the most promising applications of AI in drug design. However, further research is needed to fully explore the potential of AI in drug design and overcome some of the limitations of current approaches. Keywords: Artificial Intelligence; Drug Design; Virtual Screening; De Novo Drug Design; ADME Prediction

АНАЛІЗ ПІДХОДІВ ДО ВИДАЛЕННЯ ХМАРНОСТІ НА КОСМІЧНИХ ЗНІМКАХ ДИСТАНЦІЙНОГО ЗОНДУВАННЯ ЗЕМЛІ

The modern development of space technologies and remote sensing creates unique opportunities for solving problems in many areas, including the military. Remote sensing imagery often plays a key role in decision-making at all levels of military command, so one of the most important tasks in this context is cloud detection and extraction. This is an important stage of remote sensing data processing aimed at reconstructing information hidden by clouds. The article is devoted to the analysis of different approaches to cloud removal and improvement of the data quality. The approaches based on the use of various image processing algorithms (traditional approaches) have certain limitations associated with the frequent loss of useful information. Special attention is paid to deep learning methods, which have gained popularity in solving cloud removal problems. Deep Neural Networks show great potential for recovering information on satellite images that is hidden by clouds. This paper discusses various Deep Neural Networks architectures, such as convolutional neural networks, conditional generative adversarial networks, and their modifications. Their advantages and disadvantages are also considered. The use of such methods is more accurate and efficient compared to traditional image processing methods, as neural networks can adapt to various conditions and types of images. The analyzed disadvantages of fusing purely optical data led to the conclusion that the best approach to solving the problem of removing clouds from satellite images would be to combine optical and radar data. Despite the complexity of such an approach, it can show the greatest efficiency in solving the problem considered in this article. The challenges and prospects for further improvement of cloud removal methods on satellite images are considered. In particular, the use of artificial intelligence algorithms for automatic cloud detection and removal, as well as the need to create standardized methods for comparing and evaluating the effectiveness of different approaches. Keywords: satellite imagery; remote sensing; cloud cover; neural networks.

How have RPAS helped monitor forests and what can we apply in forest restoration monitoring?

We are currently experiencing an unprecedented moment in forest restoration, where monitoring activities in recovery areas play a vital role in problem identification and method adaptation. However, it is crucial to shift away from expensive traditional methodologies and develop technologies that enable large‐scale monitoring using more accessible and cost‐effective tools. In an effort to provide an evidence‐based perspective, we conducted a systematic review of how remotely piloted aircraft systems equipped with various sensors have been employed for forest monitoring. We consulted three databases and included 53 articles in our review. The results revealed a trend toward research in tropical ecosystems, with forest structure being the most frequently assessed attribute, and canopy height being the most widely measured structural indicator. Red‐green‐blue sensors were commonly used, both individually and in combination with others, and there was a noticeable shift toward the use of light detection and ranging. Data validation primarily relied on forest inventory methods, often involving comparisons of outputs from different sensors and the use of artificial intelligence algorithms. Despite the wide range of studies utilizing sensor‐equipped drones to analyze forest attributes, there is a notable scarcity of research specifically addressing the application of these technologies in forest restoration monitoring. Filling this research gap is essential, as employing techniques that enable large‐scale monitoring, such as aerial photogrammetry and remote sensing, aligns with current trends and contributes to global commitments to environmental restoration and conservation.

Predictive maintenance of standalone steel industrial components powered by a dynamic reliability digital twin model with artificial intelligence

The increasing use of Artificial Intelligence algorithms underscores the importance of large datasets. Recent trends highlight the need for high-quality training data, especially in scenarios where data may be outdated or insufficient. This challenge is particularly evident in applications where sensors cannot be installed or data is limited, such as in the case of steel components widely used in various industries. To address this gap, model-based approaches show promise by using advanced Digital Twin systems to generate synthetic data, closer to the real working scenarios, for training Artificial Intelligence algorithms. This paper introduces a novel Dynamic Reliability Digital Twin to model cumulative fatigue damage in steel components based on Wöhler and Manson & Halford theory and on a Monte Carlo simulation, providing a dataset for training an AI predictor to estimate remaining useful life. The results demonstrate that machine learning algorithms yield favorable outcomes when the dataset is appropriately calibrated. Therefore, a thorough understanding of the underlying physics is essential to avoid potential bias in the machine learning results.

Феномен риска в условиях применения алгоритмов искусственного интеллекта

With the advent of large language models, researchers became interested in artificial intelligence algorithms and their application in the activities of enterprises. Such algorithms have already made a significant contribution in the fields of medicine, transport and finance. It is now necessary to understand what side effects artificial intelligence algorithms may cause once they are integrated into the production of goods and services. The purpose of this work is to identify risk factors for the use of algorithms and artificial intelligence systems, assess their impact on the level of risk and offer tools to reduce the level of risk. As a result of the conducted research, the term “artificial intelligence” was clarified; algorithms for analytical, functional, interactive, textual and visual artificial intelligence were identified. The modern artificial intelligence system represents the next wave of enterprise automation. Using the methods of system economic theory and operational theory, the risk factors for the use of artificial intelligence algorithms in the object, process, design and environmental subsystems of the enterprise are classified. Anti-risk management actions are proposed to reduce the likelihood of a risk situation or to compensate for the damage caused. The changes in the activities of large companies in the field of information technology related to the change in the concept of copyright and intellectual property are described. The limitations of the introduction and application of artificial intelligence algorithms associated with the inertia of managers’ thinking and the complexity of restructuring business processes at the enterprise are shown. The necessity of regulation in the field of the introduction of artificial intelligence systems, the definition of clear boundaries and restrictions to ensure the safety of individuals and society as a whole, and their compliance with ethical standards are substantiated.