Principles Of Artificial Intelligence Research Articles

AI-Powered Personalized Mobile Education for New Zealand Students

This research endeavors to develop and assess a customized mobile education system for students in New Zealand, employing the principles of artificial intelligence (AI) and user-centered design (UCD). The objective is to overcome the limited personalization observed in current mobile education solutions by offering tailored learning content and recommendations based on individual preferences, thereby accommodating the diverse requirements of students. A mixed-methods approach will be utilized, encompassing user research, persona development, user journey mapping, design, development, and evaluation. Participants, including New Zealand students, parents, and teachers, will actively engage in multiple research phases to ensure the effective implementation of user-centered design principles. By showcasing the potential of AI-driven personalization in enhancing the learning experience for students, this study contributes to the growing utilization of AI algorithms and systems within the educational context.

Deploying artificial intelligence for anti-money laundering and asset recovery: the dawn of a new era

PurposeThis paper aims to critically examine the digital transformation of anti-money laundering (AML) and countering the financing of terrorism (CFT) in light of the Financial Action Task Force (FATF) San Jose principles, the Organisation for Economic Co-operation and Development (OECD) principles for artificial intelligence (AI) and the proposed European Union (EU) Artificial Intelligence Act. The authors argue that AI tools can revolutionize AML/CFT and asset recovery, but there is a need to strike a balance between optimizing AML efficiency and safeguarding fundamental rights.Design/methodology/approachThis paper draws on reports, legislation, legal scholarships and other open-source data on the digital transformation of AML/CFT, particularly the deployment of AI in this context.FindingsA new regulatory framework with robust safeguards is necessary to mitigate the risks associated with the use of new technologies in the AML context.Originality/valueThis study is one of the first to examine the use of AI in the AML/CFT context in light of the FATF San Jose principles, the OECD AI principles and the proposed EU AI Act.

Safeguarding Connected Health: Leveraging Trustworthy AI Techniques to Harden Intrusion Detection Systems Against Data Poisoning Threats in IoMT Environments

Internet of Medical Things (IoMT) environments introduce vast security exposures including vulnerabilities to data poisoning threats that undermine integrity of automated patient health analytics like diagnosis models. This research explores applying trustworthy artificial intelligence (AI) methodologies including explainability, bias mitigation, and adversarial sample detection to substantially enhance resilience of medical intrusion detection systems. We architect an integrated anomaly detector featuring purpose-built modules for model interpretability, bias quantification, and advanced malicious input recognition alongside conventional classifier pipelines. Additional infrastructure provides full-lifecycle accountability via independent auditing. Our experimental intrusion detection system design embodying multiple trustworthy AI principles is rigorously evaluated against staged electronic record poisoning attacks emulating realistic threats to healthcare IoMT ecosystems spanning wearables, edge devices, and hospital information systems. Results demonstrate significantly strengthened threat response capabilities versus baseline detectors lacking safeguards. Explainability mechanisms build justified trust in model behaviors by surfacing rationale for each prediction to human operators. Continuous bias tracking enables preemptively identifying and mitigating unfair performance gaps before they widen into operational exposures over time. SafeML classifiers reliably detect even camouflaged data manipulation attempts with 97% accuracy. Together the integrated modules restore classification performance to baseline levels even when overwhelmed with 30% contaminated data across all samples. Findings strongly motivate prioritizing adoption of ethical ML practices to fulfill duty of care around patient safety and data integrity as algorithmic capabilities advance.

Guidance for researchers and peer-reviewers on the ethical use of Large Language Models (LLMs) in scientific research workflows

For researchers interested in exploring the exciting applications of Large Language Models (LLMs) in their scientific investigations, there is currently limited guidance and few norms for them to consult. Similarly, those providing peer-reviews on research articles where LLMs were used are without conventions or standards to apply or guidelines to follow. This situation is understandable given the rapid and recent development of LLMs that are capable of valuable contributions to research workflows (such as OpenAI’s ChatGPT). Nevertheless, now is the time to begin the development of norms, conventions, and standards that can be applied by researchers and peer-reviewers. By applying the principles of Artificial Intelligence (AI) ethics, we can better ensure that the use of LLMs in scientific research aligns with ethical principles and best practices. This editorial hopes to inspire further dialogue and research in this crucial area of scientific investigation.

Development of Mobile Application in Assessing Commuting Accident Risk (CommuRisk) Amongst Commuters at Klang Valley

Introduction: Globally, commuting accident risks are always neglected in an organisation. There is a need to assess the impact of commuting accidents based on sociodemographic, human, vehicle, road, and environmental factors and to find suitable and effective mitigation strategies to alleviate the associated undesirable outcomes. Methods: This research was designed to develop a mobile application to assess commuting accident risk levels using artificial intelligence principles, as we are now in the 21st-century technology era. A total of 216 respondents from private and government industries participated in this study. Besides, to prove the developed application’s effectiveness, the study evaluated the effectiveness of the identified risk factor in determining the level of commuting risks predicted by respondents with the risk level calculated by the mobile application. Results: A major contribution of this paper is the effectiveness and accuracy of a mobile application known as CommuRisk. The app was developed using Android Studio and natively uses Java. There was a significant difference between with and without mobile applications in determining the level of commuting risks, and the effectiveness was proven with a (p-value = 0.001) at a 95% confidence interval with large sample size. Conclusion: Thus, this paper proved the effectiveness and accuracy of a mobile application in calculating risk levels exposed by commuters compared to risk levels predicted by commuters.

Cooperative Spectrum Sensing in Cognitive Radio Networks: A Systematic Review

Background:Spectrum is the backbone for wireless communications including internet services. Now days, the business of industries providing wired communication is constant while the business of industries dealing with wireless communications is growing very fast. There is large demand of radio spectrum for new wireless multimedia services. Although the present fixed spectrum allotment schemes do not cause any interference between users, but this fixed scheme of spectrum allocation do not allow accommodating the spectrum required for new wireless services. Cognitive radio (CR) relies on spectrum sensing to discover available frequency bands so that the spectrum can be used to its full potential, thus avoiding interference to the primary users (PU).Objectives:The purpose of this work is to present an in-depth overview of traditional as well as advanced artificial intelligence and machine learning based cooperative spectrum sensing (CSS) in cognitive radio networks.Method:Using the principles of artificial intelligence (AI), systems are able to solve issues by mimicking the function of human brains. Moreover, since its inception, machine learning has demonstrated that it is capable of solving a wide range of computational issues. Recent advancements in artificial intelligence techniques and machine learning (ML) have made it an emergent technology in spectrum sensing.Result:The result shows that more than 80% papers are on traditional spectrum sensing while less than 20% deals with artificial intelligence and machine learning approaches. More than 75% papers address the limitation of local spectrum sensing. The study presents the various methods implemented in the spectrum sensing along with merits and challenges.Conclusion:Spectrum sensing techniques are hampered by a variety of issues, including fading, shadowing, and receiver unpredictability. Challenges, benefits, drawbacks, and scope of cooperative sensing are examined and summarized. With this survey article, academics may clearly know the numerous conventional artificial intelligence and machine learning methodologies used and can connect sharp audiences to contemporary research done in cognitive radio networks, which is now underway.

Investigating Students' Perceptions towards Artificial Intelligence in Medical Education.

Implementing a reform in medical education requires students' awareness regarding the importance of artificial intelligence (AI) in modern medical practice. The objective of this study was to investigate students' perceptions of AI in medical education. A cross-sectional survey was conducted from June 2021 to November 2021 using an online questionnaire to collect data from medical students in the Faculty of Medicine at Kuwait University, Kuwait. The response rate for the survey was 51%, with a sample size of 352. Most students (349 (99.1%)) agreed that AI would play an important role in healthcare. More than half of the students (213 (60.5%)) understood the basic principles of AI, and (329 (93.4%)) students showed comfort with AI terminology. Many students (329 (83.5%)) believed that learning about AI would benefit their careers, and (289 (82.1%)) believed that medical students should receive AI teaching or training. The study revealed that most students had positive perceptions of AI. Undoubtedly, the role of AI in the future of medicine will be significant, and AI-based medical practice is required. There was a strong consensus that AI will not replace doctors but will drastically transform healthcare practices.

Evaluating Europe's push to enact AI regulations: how will this influence global norms?

ABSTRACT Artificial intelligence (AI) policy, innovation, and practice are moving ahead in rapid fashion. There is a growing mismatch between technological innovations in AI, which are advancing at a rapid rate, and normative and regulatory frameworks, which are lagging, particularly when it comes to protecting democratic values and human rights principles. National governments and multilateral institutions are attempting to catch up. At least 175 countries, firms and other organizations have produced documents listing ethical principles for AI. These efforts have proceeded in a somewhat fragmented manner, yet there are emerging signs of consolidation as the United States, Europe, and other countries begin to coalesce around shared principles. Europe, in particular, has raced ahead to draft comprehensive legislation, the Artificial Intelligence Act (AIA), to oversee these technologies and systems. What has motivated the European Union to pursue this approach? And how will this effort influence AI norms globally? This paper describes how Europe’s AI norm-building process represents an effort to ensure EU priorities are reflected in the AI governance landscape. Europe’s approach faces uncertainty. While it is likely that the AIA will meaningfully influence global AI norms, several factors may hinder its global diffusion and adoption.

Regulating Artificial Intelligence in International Investment Law

Abstract The interaction between artificial intelligence (AI) and international investment treaties is an uncharted territory of international law. Concerns over the national security, safety, and privacy implications of AI are spurring regulators into action around the world. States have imposed restrictions on data transfer, utilised automated decision-making, mandated algorithmic transparency, and limited market access. This article explores the interaction between AI regulation and standards of investment protection. It is argued that the current framework provides an unpredictable legal environment in which to adjudicate the contested norms and ethics of AI. Treaties should be recalibrated to reinforce their anti-protectionist origins, embed human-centric AI principles, and embrace expert witnesses and amicus briefs.

AI Accountability: Approaches, Affecting Factors, and Challenges

Accountability is one of core ethical principles for artificial intelligence (AI) systems. It is often cited as a must-have property especially in high-stake applications. This survey article investigates AI accountability from different key perspectives for its effective operationalization. Major approaches for achieving accountability are reviewed and typical factors affecting accountability are identified.

Regulating and testing normative structures for Artificial Intelligence: the possibilities of the Regulatory Living Lab

Abstract: This qualitative and exploratory study sought to understand how the Living Lab approach can serve as an innovative alternative to test a regulatory structure organized by principles for Artificial Intelligence (AI). The main research problem is: what would be the structuring elements of a non-legislative regulatory model that could standardize the advances in Artificial Intelligence? The units of analysis were the developments and commercialized innovative AI applications of startups from an incubator that integrates an internationally renowned technological park in Brazil. Entrepreneur perceptions of the dimensions of the Living Lab approach are described, analyzed, and compared (i.e. the interaction between multiple stakeholders, co-creation of value, user-centered research, solving real-world problems, and adopting multiple methodologies). The findings suggest that the Living Lab approach is an alternative for creating and developing innovations and applications in AI, with the active involvement of end-users while facing existing regulations and testing environments for new regulations - principles published by the European Union and OECD. An important observation that emerged from the study: the advances in Artificial Intelligence have occurred on an increasing scale, very close to exponentiality. For this reason, the need and importance of spreading the regulatory importance of these “what should be” principles, is very similar to those of the rules produced by state legislative action. In this exploratory study, it became evident that entrepreneurs feel there is a lack of clear regulatory mechanisms. The role of the Law in the creative proposition of regulatory models should be based on globally accepted principles, such as those published by the European Union and the OECD.

Ethics of Artificial Intelligence in Society

ABSTRACT: Every day, artificial intelligence (AI) is becoming more prevalent as new technologies are presented to the public with the intent of integrating them into society. However, these systems are not perfect and are known to cause failures that impact a multitude of people. The purpose of this study is to explore how ethical guidelines are followed by AI when it is being designed and implemented in society. Three ethics theories, along with nine ethical principles of AI, and the Agent, Deed, Consequence (ADC) model were investigated to analyze failures involving AI. When a system fails to follow the models listed, a set of refined ethical principles are created. By analyzing the failures, an understanding of how similar incidents may be prevented was gained. Additionally, the importance of ethics being a part of AI programming was demonstrated, followed by recommendations for the future incorporation of ethics into AI. The term “failure” is specifically used throughout the paper because of the nature in which the events involving AI occur. The events are not necessarily “accidents” since the AI was intended to act in certain ways, but the events are also not “malfunctions” because the AI examples were not internally compromised. For these reasons, the much broader term “failure” is used. KEYWORDS: Ethics; Artificial Intelligence; Agent-Deed-Consequence (ADC) Model; Principles of Artificial Intelligence; Virtue Ethics; Deontology; Consequentialism; AI Systems

EMPIRICAL STUDIES OF EMERGENCY MODE PARAMETERS AND THEIR INTERACTION IN DETERMINING THE FAULT LOCATION IN NETWORKS WITH AN ISOLATED NEUTRAL VOLTAGE OF 6(10) - 35 KV

"This article considers experimental confirmation of quantitative characteristics of factors influencing the determination of the distance to the fault location in networks with an isolated neutral voltage of 6(10)-35 kV. Experiments were carried out on a 35 kV overhead power line, the selection of optimality criteria was provided, a mathematical model, an experimental plan, a method for analyzing measurement data to damage detection devices were developed, response surfaces were constructed in the Mathcad 14 program in the Pro/ENGINEER package. According to the results of the experiment on the parameters of the emergency mode, it was found out that the largest influencing factor is the value of the transient resistance at the point of wire breakage of a non-metallic earth fault. It is proved that with a transient resistance above the value of 50 ohms, the distance to the damage site is almost impossible to determine with remote devices that register the parameters of the emergency mode. Recommendations for the development of a mathematical model with the production of logical operations on fuzzy sets of an adaptive neuro-fuzzy system are formulated. It is proposed to apply the mathematical model developed in the future as a basis for designing the element base of the device for determining the location of damage, working on the principle of artificial intelligence with an adaptive nature of measurements under changing external conditions. Key words: experiment, single-phase ground fault, fault location, overhead power line, isolated neutral, contact resistance, network isolation. "

Human-Centered Design to Address Biases in Artificial Intelligence

The potential of artificial intelligence (AI) to reduce health care disparities and inequities is recognized, but it can also exacerbate these issues if not implemented in an equitable manner. This perspective identifies potential biases in each stage of the AI life cycle, including data collection, annotation, machine learning model development, evaluation, deployment, operationalization, monitoring, and feedback integration. To mitigate these biases, we suggest involving a diverse group of stakeholders, using human-centered AI principles. Human-centered AI can help ensure that AI systems are designed and used in a way that benefits patients and society, which can reduce health disparities and inequities. By recognizing and addressing biases at each stage of the AI life cycle, AI can achieve its potential in health care.

Ethics Principles for Artificial Intelligence-Based Telemedicine for Public Health.

The use of artificial intelligence (AI) in the field of telemedicine has grown exponentially over the past decade, along with the adoption of AI-based telemedicine to support public health systems. Although AI-based telemedicine can open up novel opportunities for the delivery of clinical health and care and become a strong aid to public health systems worldwide, it also comes with ethical risks that should be detected, prevented, or mitigated for the responsible use of AI-based telemedicine in and for public health. However, despite the current proliferation of AI ethics frameworks, thus far, none have been developed for the design of AI-based telemedicine, especially for the adoption of AI-based telemedicine in and for public health. We aimed to fill this gap by mapping the most relevant AI ethics principles for AI-based telemedicine for public health and by showing the need to revise them via major ethical themes emerging from bioethics, medical ethics, and public health ethics toward the definition of a unified set of 6 AI ethics principles for the implementation of AI-based telemedicine. (Am J Public Health. 2023;113(5):577-584. https://doi.org/10.2105/AJPH.2023.307225).

Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer.

Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.

Hacking and Artificial Intelligence in Radiology: Basic Principles of Data Integrity and Security

Dr. Ritenour is Professor, Department of Radiology and Radiological Science, Medical University of South Carolina, 96 Jonathan Lucas St, MSC 323, 210 CSB, Charleston, SC 29425; E-mail: [email protected]. After participating in this educational activity, the radiologist should be better able to describe the principles of artificial intelligence and data security and explain how these features enhance the efficacy and reliability of the diagnostic process. All authors, faculty, and staff have no relevant financial relationships with any ineligible organizations regarding this educational activity. Lippincott Continuing Medical Education Institute, Inc., is accredited by the Accreditation Council for Continuing Medical Education to provide continuing medical education for physicians. Lippincott Continuing Medical Education Institute, Inc., designates this enduring material for a maximum of 2 AMA PRA Category 1 Credits™. Physicians should claim only the credit commensurate with the extent of their participation in the activity. To earn CME credit, you must read the CME article and complete the quiz and evaluation on the enclosed answer form, answering at least seven of the 10 quiz questions correctly. This continuing medical education activity expires on February 27, 2025.

Analysis of Sustainability of IT Team Development in an Organization

The paper shows the results of experiments focused on the analysis of sustainability of a quality work team in an IT organization. The analysis has been carried out based on the evaluation of the new real dates. Data consists of competences and direction of the strategy of individual specialists, as well as the strategy of the IT team as a whole. The principles of the SWOT analysis and of confrontational matrix have been used to evaluate the strategy and focus of individuals. The next step of the solution assumes the use of the principles of artificial intelligence in the process of solving relations within organizations.

Im2Graph: A Weakly Supervised Approach for Generating Holistic Scene Graphs from Regional Dependencies

Conceptual representations of images involving descriptions of entities and their relations are often represented using scene graphs. Such scene graphs can express relational concepts by using sets of triplets ⟨subject—predicate—object⟩. Instead of building dedicated models for scene graph generation, our model tends to extract the latent relational information implicitly encoded in image captioning models. We explored dependency parsing to build grammatically sound parse trees from captions. We used detection algorithms for the region propositions to generate dense region-based concept graphs. These were optimally combined using the approximate sub-graph isomorphism to create holistic concept graphs for images. The major advantages of this approach are threefold. Firstly, the proposed graph generation module is completely rule-based and, hence, adheres to the principles of explainable artificial intelligence. Secondly, graph generation can be used as plug-and-play along with any region proposition and caption generation framework. Finally, our results showed that we could generate rich concept graphs without explicit graph-based supervision.

How do nurses work in chronic management in the age of artificial intelligence? development and future prospects.

AI is undeniably revolutionizing medical research and patient care across diverse fields. Chronic disease nursing care, a pivotal aspect of clinical management, has significantly reaped the benefits of AI across numerous dimensions. Understanding the operational principles of artificial intelligence before implementation is crucial, avoiding indiscriminate replacement of all tasks with AI. Nurses serve as the primary force in symptom group research, expanding beyond diabetes to encompass various chronic diseases; their primary responsibility involves recording patients' daily symptoms and vital signs. However, a substantial portion of current AI research excludes nurses from the developmental phase, encompassing them solely in user and feedback populations. The comprehensive design of the symptom analysis and long-term management approach necessitates the guidance and oversight of nurses; however, their current insufficient involvement might stem from nursing staff's comparatively limited comprehension of AI and their ambiguous perception of their role's value in AI. Therefore, an imperative exploration of nurses' roles in symptom analysis and long-term management, leveraging the latest research in these areas, is vital to pinpoint breakthroughs in nurses' AI involvement in the future.