Artificial Intelligence Systems Research Articles

Rules of engagement: ethical issues and value chain introspection in Artificial Intelligence systems

Rules of engagement: ethical issues and value chain introspection in Artificial Intelligence systems

The always changing data problem of using AI in manufacturing : Using synthetic data from the digital twin to feed AI models

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.

Real-World evaluation of an AI triaging system for chest X-rays: A prospective clinical study

Chest X-rays (CXRs) are crucial for diagnosing and managing lung conditions. While CXR is a common and cost-effective diagnostic tool, interpreting the high volume of CXRs is challenging due to workforce limitations. Artificial intelligence (AI) offers promise in enhancing efficiency and accuracy. However, real-world applicability and generalizability across diverse patient cohorts remain areas of concerns. In our study, the LUNIT INSIGHT CXR Triage software was evaluated in a diverse patient cohort. Forty-three radiologists, blinded to AI results, assessed CXRs categorized into normal, non-urgent, and urgent using a 3-tier classification system. Performance metrics and turnaround times were analyzed.The AI system demonstrated sensitivity of 89% for normal CXRs, specificity of 93%, PPV of 83%, and NPV of 95%, with an F1 score of 0.86 and an AUC of 0.91. For non-urgent CXRs, sensitivity and specificity were 93% and 91%, with PPV and NPV at 94% and 89%, respectively, and an F1 score of 0.94 and an AUC of 0.92. In the urgent category, sensitivity was 82%, specificity 99%, PPV 90%, and NPV 98%. Subgroup analysis revealed consistently high accuracy across various age groups (Young, Adult, Senior), genders, and ethnicities (Chinese, Malay, Indian, Others), with sensitivity, specificity, and AUC consistently above 84%. The AI system also significantly reduced turnaround times across all subgroups, indicating its robust performance and generalizability in diverse healthcare settings.

Artificial Intelligence & the Capacity for Discrimination: The Imperative Need for Frameworks, Diverse Teams & Human Accountability

The increasing integration of Artificial Intelligence (AI) in various industries has led to concerns about how these systems can perpetuate discrimination, particularly in fields like employment, healthcare, and public policy. Multiple academic and business perspectives on AI discrimination, focusing on the need for global policy coordination and ethical oversight to mitigate biased outcomes, ask for our technical innovators to create contingencies that will better protect humanity’s experience with AI’s ever-expanding reach. Central to the key constructs such as biased datasets, algorithmic transparency, and the global governance of AI systems can function as a harmful drawback to these systems. Without adequate data governance and transparency, AI systems can perpetuate discrimination. AI's ability to discriminate stems primarily from biased data and the opacity of machine learning models, necessitating proactive research and policy implementation on a global scale. These frameworks must transcend the limitations of the experiences or perspectives of their programmers to ensure that AI innovations are ethically sound and that their use in global organizations adheres to principles of fairness and accountability. This synthesis will explore how these articles advocate for comprehensive, continuous monitoring of AI systems and policies that address both local and international concerns, offering a roadmap for organizations to innovate responsibly while mitigating the risks of AI-driven discrimination.

Этические и правовые аспекты цифрового взаимодействия органов местного самоуправления и граждан

The article examines the issues of digital interaction between local governments and citizens, taking into account legal and ethical aspects. The risk factors in relation to the development of artificial intelligence systems are identified from the point of view of the multi-vector nature of its impact on emerging legal relations. Pessimism about artificial intelligence is indicated by a risk factor that may affect the pace of digital technology adoption.

Complex Scene Understanding and Object Detection Algorithm Assisted by Artificial Intelligence

The purpose of this paper is to study the algorithm of object detection and scene understanding in complex scenes assisted by AI(Artificial Intelligence), so as to improve the machine's perception of the surrounding environment. Aiming at occlusion and dense scenes, this paper proposes a series of innovative algorithms by introducing technologies such as multi-scale detection, occlusion processing and real-time optimization. Experimental results show that the proposed algorithm has achieved excellent performance on several public data sets. Compared with the current mainstream object detection algorithm -YOLO, the method proposed in this paper has a significant improvement in accuracy. Especially in multi-scale object detection, occlusion and dense scene processing and real-time optimization, the proposed algorithm shows obvious advantages. The research results provide an effective solution for object detection and scene understanding in complex scenes, and promote the application of AI system in related fields.

Outsourcing, Augmenting, or Complicating: The Dynamics of AI in Fact-Checking Practices in the Nordics

The practice of fact-checking involves using technological tools to monitor online disinformation, gather information, and verify content. How do fact-checkers in the Nordic region engage with these technologies, especially artificial intelligence (AI) and generative AI (GAI) systems? Using the theory of affordances as an analytical framework for understanding the factors that influence technology adoption, this exploratory study draws on insights from interviews with 17 professionals from four Nordic fact-checking organizations. Results show that while AI technologies offer valuable functionalities, fact-checkers remain critical and cautious, particularly toward AI, due to concerns about accuracy and reliability. Despite acknowledging the potential of AI to augment human expertise and streamline specific tasks, these concerns limit its wider use. Nordic fact-checkers show openness to integrating advanced AI technology but emphasize the need for a collaborative approach that combines the strengths of both humans and AI. As a result, AI and GAI-based solutions are framed as “enablers” rather than comprehensive or end-to-end solutions, recognizing their limitations in replacing or augmenting complex human cognitive skills.

Inovasi Teknologi Digital sebagai Penggerak Reformasi dalam Sistem Pendidikan

Digital technology innovation has become the main driver in reforming the education system in the modern era. The use of digital technologies, such as online learning platforms, artificial intelligence, and data-based education management systems, has changed the way education is learned, taught, and managed. These innovations not only increase the accessibility and flexibility of learning, but also enable the personalization of the learning experience for students. This article aims to examine the role of digital technological innovation in driving education system reform, as well as highlighting the opportunities and challenges faced in the transformation process. The results of this research conclude that digital technology innovation has great potential to encourage education system reform, as long as it is accompanied by supportive policies, increasing the competency of teaching staff, and equitable development of technological infrastructure. It is hoped that this reform can create an education system that is more inclusive, adaptive and relevant to future challenges.

Interpretable logical-probabilistic approximation of neural networks

The paper proposes the approximation of DNNs by replacing each neuron by the corresponding logical-probabilistic neuron. Logical-probabilistic neurons learn their behavior based on the responses of initial neurons on incoming signals and discover all logical-probabilistic causal relationships between the input and output. These logical-probabilistic causal relationships are, in a certain sense, most precise – it was proved in the previous works that they are theoretically (when probability is known) can predict without contradictions. The resulting logical-probabilistic neurons are interconnected by the same connections as the initial neurons after replacing their signals on true/false. The resulting logical-probabilistic neural network produces its own predictions that approximate the predictions of the original DNN. Thus, we obtain an interpretable approximation of DNN, which also allows tracing of DNN by tracing its excitations through the causal relationships. This approximation of DNN is a Distillation method such as Model Translation, which train alternative smaller interpretable models that mimics the total input/output behavior of DNN. It is also locally interpretable and explains every particular prediction. It explains the sequences of logical probabilistic causal relationships that infer that prediction and also show all features that took part in this prediction with the statistical estimation of their significance. Experimental results on approximation accuracy of all intermedia neurons, output neurons and softmax output of DNN are presented, as well as the accuracy of obtained logical-probabilistic neural network. From the practical point of view, interpretable transformation of neural networks is very important for the hybrid artificial intelligent systems, where neural networks are integrated with the symbolic methods of AI. As a practical application we consider smart city.

Reconstructing AI Ethics Principles: Rawlsian Ethics of Artificial Intelligence

The popularisation of Artificial Intelligence (AI) technologies has sparked discussion about their ethical implications. This development has forced governmental organisations, NGOs, and private companies to react and draft ethics guidelines for future development of ethical AI systems. Whereas many ethics guidelines address values familiar to ethicists, they seem to lack in ethical justifications. Furthermore, most tend to neglect the impact of AI on democracy, governance, and public deliberation. Existing research suggest, however, that AI can threaten key elements of western democracies that are ethically relevant. In this paper, Rawls’s theory of justice is applied to draft a set of guidelines for organisations and policy-makers to guide AI development towards a more ethical direction. The goal is to contribute to the broadening of the discussion on AI ethics by exploring the possibility of constructing AI ethics guidelines that are philosophically justified and take a broader perspective of societal justice. The paper discusses how Rawls’s theory of justice as fairness and its key concepts relate to the ongoing developments in AI ethics and gives a proposition of how principles that offer a foundation for operationalising AI ethics in practice could look like if aligned with Rawls’s theory of justice as fairness.

Development of an intelligent model for management of the requirements of marketing projects of the pharmaceutical industry

The object of research is the processes of managing the requirements of the marketing project using means of intelligent data analysis and artificial intelligence systems. Marketing projects include a large number of projects of various types, formats and scales. The work considers projects that are initiated at an existing pharmaceutical enterprise that produces drugs for the local market. Over a period of time, the company needs to introduce new dosage forms to the market. It is not about the development of new innovative drugs. Usually, these are new mixtures of known components, but according to the results of global research, adding new components to the formulation gives a new, additional effect. Also, it can be a new form, for example, a spray or syrup for children, etc. Nevertheless, in order to produce such a new product and bring it to the market, to make a profit, it is necessary to conduct a study of the local market in order to find analogues, consumer expectations and predict future cash flows from sales of new drugs. In order to form requirements for the project of creating and manufacturing a new medicinal form, the company initiates marketing projects. Such marketing projects are the subject of research. As a result of the marketing project of the analysis of the drug market, it is necessary to form the contours of a new product and the requirements for the project of creating this product – a new medicinal product. Collecting and forming requirements is not a trivial task. Therefore, in the work, it is proposed to apply means of intelligent data analysis, which will be implemented on artificial intelligence systems. As a result, an intelligent model for managing the requirements of marketing projects of the pharmaceutical industry was developed. The non-triviality of the task of requirements formation is based on the fact that it is multi-criteria. The desired requirements and, accordingly, the future product of the project depend on: the lack of a similar dosage form on the local market, the presence of theoretical and practical pharmacological developments in this direction, the technical and financial capabilities of the enterprise, the expectations of stakeholders and consumers of the product. The use of intelligent tools will allow to avoid a conflict of requirements and to develop a medicine that will not only meet the expectations of all participants of the local pharmaceutical market, but also provide profit to the developing company.

Possibilities and challenges of Artificial Intelligence in the teaching and learning process of Journalism Writing. The experience in Spanish universities

The research aims to assess the possibilities and challenges of artificial intelligence (AI) for the development of the teaching and learning of journalistic writing techniques in the university educational context. The main objective consists of analysing the abilities of AI to solve the composition of different journalistic texts¸ both informative and opinion, and the quality of the result, as well as other applications of AI in journalistic writing. The text tries to contribute to the knowledge on the use of AI by teachers and students in the teaching and learning process. A mixed methodology has been used to develop the research: on the one hand, descriptive and comparative, based on surveys to university professors in Spain specialised in this subject and to students taking related contents in their studies, also in this context; on the other hand, an experimental research is included among professors trained in the field of Journalistic Writing, who are asked for the qualified correction of an exam, anonymous, actually carried out with the AI system, ChatGPT. After weighing the results, each of them is interviewed in depth about the quality of the texts, their strengths, and weaknesses. The results show that AI is beginning to be used in the university context by students and professors. In the specific field of journalistic writing, its use for the moment is limited to assisting tasks in the writing process or suggesting topics, while the automatic generation of content is not widely accepted due to the ethical implications that it entails, especially those related to the originality of content. Teachers value the potential of this technology for writing simple and informative pieces but observe limitations at deeper levels of knowledge and in hybrid texts.

Maximising Synergy: The Benefits of a Joint Implementation of Knowledge Management and Artificial Intelligence System Standards

Implementing management systems in organisations of all types and sizes often raises the following question: “What benefits will this bring?” Initial resistance and criticism are common as potential challenges are identified during the implementation process. To address this, it is essential to highlight the advantages of these systems and engage stakeholders in supporting management efforts. While the planning, implementation, use, maintenance, auditing, and improvement of management systems are generally voluntary, certification is frequently driven by external factors, particularly customer demands. Employees also stand to gain significantly, with knowledge and information serving as valuable resources, especially for leveraging artificial intelligence. This article explores the management’s readiness to adopt and fully utilise two management systems based on international standards: the ISO 30401 Knowledge management system (KMS) and the ISO/IEC 42001 Artificial intelligence management system (AIMS). Through interviews, we assess the challenges and solutions associated with implementing these systems, whether planned or partially adopted. The findings illustrate the synergistic benefits of integrating the KMS and AIMS, demonstrating how their combined use can enhance Integrated Management Systems (IMSs). Such integration supports comprehensive planning, operation, and performance evaluation of processes and services while also promoting continuous improvement.

AI Methods and Algorithms for Diagnosis of Intestinal Parasites: Applications, Challenges and Future Opportunities

Artificial intelligence (AI) transforms intestinal parasite diagnosis, particularly through deep learning models like convolutional neural networks (CNNs). This paper reviews the application of AI, especially CNNs, in automating parasite detection and classification from microscopic images. Integrating AI into parasitology diagnostics speeds up the process, reduces human error, and enhances treatment and patient outcomes. However, there is a need for more datasets reflecting the African context to ensure accurate ground-truthing, particularly in low- and middle-income countries (LMICs) across Africa. Most AI models for medical diagnosis are trained on datasets from high-income countries, which may not capture the unique epidemiological, genetic, and environmental factors prevalent in African populations. This can lead to less accurate diagnoses and treatment recommendations in African LMICs. For example, intestinal parasitic infections are common in many African regions, yet the datasets used to train AI models often lack sufficient representation from these areas. Developing datasets that reflect the diverse African context is crucial for improving the accuracy and reliability of AI-based diagnostic tools. Issues like overfitting, data privacy, and cost also require attention. Collaboration between researchers, healthcare professionals, and technologists is essential to address these challenges. Standardized protocols for data collection, model training, and validation are necessary for reliable AI systems. Combining AI with traditional techniques holds promise for better parasite diagnosis, ultimately improving African healthcare outcomes

Innovative approaches to social impact measurement: a focus on the potential of artificial intelligence solutions

Purpose The existing literature highlights challenges in measuring social impact within social and solidarity economy organisations, particularly regarding the social return on investment (SROI) methodology. This paper aims to address the barriers to SROI implementation while exploring the potential of artificial intelligence (AI) in enhancing the measurement of social impact. Design/methodology/approach This review-based paper synthesises research on SROI methodology limitations and recent AI developments while focusing on ethical considerations. Drawing from these domains, the study constructs a conceptual framework to guide future research. Findings The study identifies necessary enhancements to existing AI systems for social impact measurement and explores how advances in generative AI could refine current tools and address SROI constraints. It advocates for open AI models to address ethical concerns. Originality/value This study pioneers the integration of social impact assessment and AI, an innovative intersection in the academic literature. The growing disparity between academia and the rapidly evolving AI industry is highlighted and scholarly discourse is enriched through theoretical deliberations and innovative technological solutions.

Harnessing AI for Next-Generation Financial Fraud Detection: A Data-Driven Revolution

Artificial Intelligence in Financial Fraud Detection: A Comprehensive Approach to Enhancing Financial Security The rise of artificial intelligence (AI) offers an opportunity to significantly strengthen financial security by combating financial fraud, which has become increasingly complex and widespread. Traditional detection methods are often insufficient in identifying and preventing fraudulent activities, prompting a shift towards AI-based solutions. This study explores the application of AI, particularly machine learning algorithms, in improving the accuracy and efficiency of fraud detection. By analyzing large financial datasets, AI can detect anomalies that may indicate fraudulent behavior more effectively than traditional approaches. This research adopts a two-phase methodology. The first phase involves a thorough review of existing financial fraud detection methods, comparing traditional techniques with AI-based models to identify gaps. Various machine learning approaches, including supervised, unsupervised, and deep learning algorithms, are reviewed for their effectiveness in detecting fraud. The second phase involves developing and testing an AI model to identify fraudulent patterns within transactional data. The model uses machine learning algorithms to process vast datasets and detect deviations from typical financial behaviors, flagging potentially fraudulent activities. The expected results indicate that AI systems can outperform traditional fraud detection methods by significantly reducing false positives and improving the detection rate of genuine fraud. This reduction in false positives is vital for financial institutions, as it reduces unnecessary investigations and saves valuable resources. Additionally, enhanced fraud detection protects both institutions and consumers from financial losses.The findings of this study aim to provide financial institutions with practical insights into the implementation of AI-driven fraud detection systems. Furthermore, the research highlights the need for continuous refinement of AI models to adapt to the evolving nature of financial fraud. By leveraging AI technologies, financial institutions can revolutionize their approach to fraud detection, making financial systems more secure and responsive to emerging threats.

End user experience of a widely used artificial intelligence based sepsis system.

Research on the Epic Sepsis System (ESS) has predominantly focused on technical accuracy, neglecting the user experience of healthcare professionals. Understanding these experiences is crucial for the design of Artificial Intelligence (AI) systems in clinical settings. This study aims to explore the socio-technical dynamics affecting ESS adoption and use, based on user perceptions and experiences. Resident doctors and nurses with recent ESS interaction were interviewed using purposive sampling until data saturation. A content analysis was conducted using Dedoose software, with codes generated from Sittig and Singh's and Salwei and Carayon's frameworks, supplemented by inductive coding for emerging themes. Interviews with 10 healthcare providers revealed mixed but generally positive or neutral perceptions of the ESS. Key discussion points included its workflow integration and usability. Findings were organized into 2 main domains: workflow fit, and usability and utility, highlighting the system's seamless electronic health record integration and identifying design gaps. This study offers insights into clinicians' experiences with the ESS, emphasizing the socio-technical factors that influence its adoption and effective use. The positive reception was tempered by identified design issues, with clinician perceptions varying by their professional experience and frequency of ESS interaction. The findings highlight the need for ongoing ESS refinement, emphasizing a balance between technological advancement and clinical practicality. This research contributes to the understanding of AI system adoption in healthcare, suggesting improvements for future clinical AI tools.

Hafnia-based neuromorphic devices

The excellent complementary metal-oxide-semiconductor compatibility and rich physicochemical properties of hafnia-based materials, in particular the unique ferroelectricity that surpasses of conventional ferroelectrics, make hafnia-based devices promising candidates for industrial applications. This Perspective examines the fundamental properties of hafnia-based materials relevant to neuromorphic devices, including their dielectric, ferroelectric, antiferroelectric properties, and the associated ultra-high oxygen-ion conductivity. It also reviews neuromorphic devices developed leveraging these properties, such as resistive random-access memories, ferroelectric random-access memories, ferroelectric tunnel junctions, and (anti)ferroelectric field-effect transistors. We also discuss the potential of these devices for mimicking synaptic and neuronal functions and address the challenges and future research directions. Hafnia-based neuromorphic devices promise breakthrough performance improvements through material optimization, such as crystallization engineering and innovative device configuration designs, paving the way for advanced artificial intelligence systems.

Ethical Implications of AI in Financial Services: Bias, Transparency, and Accountability

This article examines the ethical implications of artificial intelligence (AI) in financial services, focusing on issues of bias, transparency, and accountability. As AI adoption in finance grows rapidly, with 85% of institutions now using AI, it brings both tremendous benefits and significant ethical challenges. The article explores how AI can perpetuate biases in lending and credit scoring, the "black box" problem of opaque AI decision-making, and the complexities of establishing accountability for autonomous AI systems. It analyzes strategies for mitigating these issues, including developing interpretable AI models, implementing robust governance structures, and creating industry-specific ethical guidelines. The discussion highlights the critical need for evolving regulatory frameworks and ethical standards to ensure responsible AI use while harnessing its potential to transform financial services.

Clinical Validation of Deep Learning for Segmentation of Multiple Dental Features in Periapical Radiographs.

Periapical radiographs are routinely used in dental practice for diagnosis and treatment planning purposes. However, they often suffer from artifacts, distortions, and superimpositions, which can lead to potential misinterpretations. Thus, an automated detection system is required to overcome these challenges. Artificial intelligence (AI) has been revolutionizing various fields, including medicine and dentistry, by facilitating the development of intelligent systems that can aid in performing complex tasks such as diagnosis and treatment planning. The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on periapical radiographs. A dataset comprising 1000 periapical radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists. A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.7-0.73), implants (0.97-0.98), restored teeth (0.85-0.89), teeth with fixed prosthesis (0.92-0.94), and missing teeth (0.82-0.85). The automatic detection by the AI system was comparable to the oral radiologists and may be useful for automatic identification in periapical radiographs.