Ethical Considerations Research Articles

DISCOVERING THE FUTURE AND IMPACT OF ARTIFICIAL INTELLIGENCE: A QUALITATIVE EXPLORATION

This study explores public perceptions of Artificial Intelligence (AI), focusing on its perceived capabilities, potential risks, and societal implications. A questionnaire consisting of 15 questions was distributed to five respondents, yielding valuable insights into how individuals view AI's role in the future. The results indicate a general understanding of AI as a sophisticated, robot-like technology capable of mimicking human behavior, though not fully replicating human consciousness. While most respondents acknowledged AI’s potential to advance technology and improve daily life, there were concerns regarding its safety and the threat it poses to human employment. Specifically, three respondents expressed fear that AI could lead to global job displacement, highlighting the ongoing debate surrounding the automation of routine tasks. Despite these concerns, all respondents agreed that AI is essential for human advancement and should continue to evolve. Additionally, the study revealed that AI is already integrated into participants' lives, through gadgets and search engines like Google, demonstrating its growing presence in everyday technology. However, there was a divide on the level of intimidation AI causes, with four respondents feeling uneasy about its impact, while one remained unperturbed. The study concludes that while AI offers significant benefits, such as improving productivity and efficiency, its development must be approached with caution, ensuring ethical considerations and regulatory oversight to mitigate potential risks. These findings underscore the need for ongoing dialogue and responsible AI governance to balance innovation with societal well-being.

The Role of Technology in Mental Health: Evaluating Digital Interventions for Psychological Well-being

Digital health interventions (DHIs) have emerged as a promising approach to address mental health issues across various demographics, including different age groups, cultural contexts, and socio-economic backgrounds. This review aims to critically examine the role of digital interventions in mental health, focusing on their effectiveness, benefits, and limitations compared to traditional therapies. The review seeks to assess various digital tools, including mobile apps, online therapy platforms, AI-driven solutions, virtual reality (VR) therapies, and wearable technologies, to determine their impact on mental health outcomes. In addition, the review explores the challenges of implementing these technologies, such as data privacy concerns, the digital divide, and regulatory issues, while highlighting opportunities for innovation and integration into traditional healthcare systems. Policymakers are faced with the daunting challenges of managing rapid innovation against concerns with respect to data privacy and security and ethical considerations related to applications of AI and IoT.

Exploring Data Science Techniques for Fraud Detection

Fraud detection has become increasingly critical as online transactions and digital interactions proliferate across industries. This paper explores advanced data science methodologies and machine learning algorithms for identifying fraudulent activities in real-time, addressing challenges like data imbalance, feature engineering, and model evaluation. It provides insights into traditional and emerging fraud detection techniques and emphasizes future directions for research and implementation. Key findings and ethical considerations are also discussed.

Generative AI and Education: A Symbiotic Relationship

Generative AI, with its capacity to create diverse content formats, holds immense potential to revolutionize the educational landscape. This research paper delves into the multifaceted ways in which generative AI can enhance teaching and learning, fostering a symbiotic relationship between technology and human ingenuity. Personalized Learning: Generative AI can analyse vast datasets of student performance and engagement to create adaptive learning paths, tailoring instruction to individual needs and preferences. This personalized approach ensures that each learner receives the optimal level of support and challenge, maximizing their potential for growth and development. Engaging Learning Experiences AI-powered tools can generate a wide range of interactive and immersive learning materials, such as simulations, virtual labs, and gamified experiences. These innovative resources can make education more engaging and enjoyable, capturing students' attention and motivating them to actively participate in the learning process. Streamlining Administrative Tasks Generative AI can automate time-consuming administrative tasks, such as grading assignments, providing personalized feedback, and generating comprehensive reports on student progress. By freeing up valuable time and resources, educators can focus on more meaningful interactions with students, fostering a deeper connection and a more supportive learning environment. Ethical Considerations While generative AI offers numerous benefits, it is essential to address potential challenges and ethical concerns. Data privacy, algorithmic bias, and the responsible use of AI are critical issues that must be carefully considered. By establishing clear guidelines, developing robust safeguards, and providing educators with the necessary training and support, we can mitigate risks and ensure that AI is used ethically and effectively in education.

From Pixels to Predictions: A Comprehensive Survey of Image Classification

Image classification, a fundamental task in computer vision, has undergone significant evolution over the years, driven by advancements in deep learning and machine learning techniques. This paper presents a comprehensive survey of image classification techniques, covering its journey from early methods to state-of-the-art approaches and future directions. We delve into the fundamentals of image classification, including traditional methods and the pivotal role of Convolutional Neural Networks (CNNs). The survey explores advanced techniques such as transfer learning, attention mechanisms, and multimodal learning, along with their applications across various domains including healthcare, autonomous vehicles, social media, and more. Additionally, future trends and directions in image classification are discussed, focusing on weakly supervised learning, multimodal learning, continual learning, and ethical considerations. Through this survey, we aim to provide insights into the past, present, and future of image classification, highlighting its significance, challenges, and promising avenues of research and application.

The impact of regenerative medicine and tissue engineering on US healthcare

Regenerative medicine and tissue engineering are transformative fields with the potential to address some of the most pressing challenges in modern healthcare. This review explores the current field of regenerative medicine in the United States, focusing on its foundational principles, clinical breakthroughs, economic impact, regulatory frameworks, and integration challenges. Key advancements are highlighted across stem cell therapies, engineered tissues and organs, biomaterials, scaffolds, and gene therapy, with case studies illustrating successful applications. Additionally, this review assesses the economic implications, including market growth, treatment costs, and the shifting dynamics of insurance coverage and funding, which collectively shape the financial feasibility of these innovations. The paper also identifies critical barriers to widespread adoption, such as infrastructure limitations, training requirements for healthcare providers, ethical considerations, and the need for standardization in quality control. In examining future directions, the review emphasizes emerging technologies, strategic policy recommendations, and essential research priorities that could drive sustainable integration into healthcare systems. By providing a comprehensive overview of these areas, this paper highlights the potential long-term impact of regenerative medicine on public health and highlights the pathways necessary for its safe, effective, and equitable implementation in the United States.

Exploring the frontiers of cryonics: Feasibility, benefits, and future impact on humanity

This paper explores the scientific, ethical, and technological dimensions of cryonics—the preservation of human remains at ultra-low temperatures with the hope of future revival when medical advancements permit. Originating from Robert Ettinger's 1960s proposition, cryonics challenges traditional notions of mortality. This study examines current cryonics technology, particularly vitrification methods used to prevent ice crystal formation during freezing, as practiced by institutions like the Cryonics Institute and Alcor Life Extension Foundation. While successful human revival has yet to be achieved, progress in preserving cells and organs offers valuable insights. The feasibility is assessed by analyzing scientific possibilities and limitations, ethical and legal considerations, and technological challenges such as cellular damage and the complexities of restoring consciousness. Potential benefits include medical and therapeutic advancements, life extension, improved organ preservation, and psychological and societal impacts. Revival prospects are explored through future technologies like nanotechnology and regenerative medicine, alongside statistical probabilities and expert predictions. The paper concludes by emphasizing the need for interdisciplinary collaboration, ethical frameworks, and continued research to navigate challenges and responsibly harness the potential of cryonics, ultimately shaping its future role in humanity.

Ethical and epistemological considerations in applying moral injury (MI) concepts to refugee populations.

Empirical research on moral injury (MI) has rapidly evolved since 2009. Originally developed to address the moral dimensions of traumatic experiences among US veterans, MI has also found application in the context of traumatized refugees. This paper delves into the ethical and epistemological questions that arise when applying a concept originally rooted in a qualitatively distinct experience and a demographically different population to refugees. It is argued that the prevailing clinical and psychological conceptualization of MI may not adequately accommodate the unique needs and experiences of refugee populations. This examination underscores the imperative of conceptualizing to better serve the ethical and epistemic demands of refugee communities.

Addressing data needs crucial for improving infant, child and youth mental health and substance-related harms in Aotearoa New Zealand: key design and ethical considerations for future research.

To evaluate the available data on mental health and substance-related harm among infants, children and young people (ICY) in Aotearoa New Zealand. A scoping review was undertaken to take stock of current data and identify gaps. Although there are quality studies, there is a lack of comprehensive, contemporary population-based data to monitor the prevalence and magnitude of mental health conditions and substance use-related harm for children and young people. Existing data are inconsistently measured and are not centrally located or available for all age groups, particularly infants and children. Whānau/family units are seldom considered or prioritised. Aotearoa New Zealand lacks accurate, up-to-date, comprehensive ICY mental health and substance use data to inform investment, service delivery and evidence-based policy. We advocate for enhanced surveillance and monitoring through population-based mental health and addiction studies with Indigenous and other locally designed measures, and propose key design and ethical considerations for future research. Future research must prioritise Māori and other priority groups, with non-stigmatising, strengths-based approaches. Addressing these data gaps presents a critical opportunity to improve outcomes for children, young people and their whānau.

Explore the Relationship between Personality (e.g. Big Five Personality Traits) and Job Performance, Leadership or Career Success

This study deals with how personality traits, including conscientiousness, extraversion, and emotional stability, play a role in job performance, leadership effectiveness, and career success. The methodology has been the carefully designed survey of employees from various sectors in Punjab, Pakistan, including professionals at different stages of their careers and leaders. All types of professionals were well represented through drawing the population from education, healthcare business, and government sectors. The stratified random sampling method was employed to generate a sample size that would ensure enough representation from each of the subgroups, which are divided by the job role, industry, and experience. The gathered data were subjected to an analysis using various statistical tools, such as regression analysis, t-tests, and ANOVA. Conscientiousness in relation to job performance was examined via regression analysis. Leadership efficacy between extraverts and non-extraverts was measured using the t-tests. ANOVA was employed to examine the differences in career success among individuals exhibiting varying levels of emotional stability. Ethical considerations were meticulously adhered to, guaranteeing participant consent, privacy, and the right to withdraw at any stage of the study. The findings indicated that conscientiousness served as a positive predictor of job performance, while extraversion was correlated with enhanced leadership effectiveness, and emotional stability was significantly related to career success. These findings stress the relevance of personality traits in shaping professional results. The results indicate that personality assessments should be integrated into recruitment, leadership development, and career counseling practices.

Research on the Status Quo of Generative AI Tools Used by Pre-service Teachers

Through excellent text generation ability and rich knowledge reserves, generative AI has been widely discussed among pre-teachers in the past two years, and the analysis of the status of generative AI tools is particularly important. This study of 280 Chinese Qingdao University teachers questionnaire is analyzed, discusses the pre-professional teachers use the main scenarios, influencing factors of artificial intelligence, the view of former teachers use generative artificial intelligence tools on self-efficacy, and generative artificial intelligence tools for pre-service teachers cause certain anxiety. The following conclusions are drawn: generative artificial intelligence tools have improved the learning and teaching of teachers to a certain extent, examples are improving the efficiency of lesson preparation, enriching course design, summarizing knowledge points, etc. However, it still needs to be strengthened in the course feedback and personalized analysis. At the same time, it can be preliminarily concluded from this study that if people want to promote the further widespread application of generative AI tools in the pre-service group of teachers, the accuracy and some ethical considerations of generative AI tools need to be addressed. This study provides insights into the nexus between the characteristics of generative AI influencing pre-service teachers.

Preserving geological heritage: addressing destructive sampling and ethical considerations along the unique Port Macquarie Coastal Tract, mid North Coast of NSW, Australia

Preserving geological heritage: addressing destructive sampling and ethical considerations along the unique Port Macquarie Coastal Tract, mid North Coast of NSW, Australia

Digital preservation strategies for historical records in the age of AI and the metaverse

Purpose This study aims to explore digital preservation strategies for historical records, focusing on the integration of artificial intelligence (AI) and the metaverse. It examines how AI-driven technologies and immersive virtual spaces can enhance the accessibility, integrity and usability of digitized historical archives. Design/methodology/approach The research draws on literature from AI, digital preservation and archival studies to analyze current trends and challenges in preserving digital records. It evaluates the role of AI in automating preservation processes and examines the potential of the metaverse for interactive engagement with historical data. Ethical considerations regarding data privacy and ownership are also discussed. Findings AI enhances digital preservation by streamlining digitization and predictive record management. The metaverse offers a novel platform for immersive engagement with historical records, allowing users to interact with and experience archives in dynamic, virtual environments. However, challenges related to data ownership, privacy and the integrity of historical records must be addressed to ensure sustainable preservation efforts. Originality/value This paper contributes to the growing body of research on the intersection of emerging technologies and digital preservation. It offers new insights into how AI and the metaverse can reshape archival practices, highlighting both opportunities and ethical challenges for institutions tasked with preserving historical records in an increasingly digital world.

Applications of Vedic Mathematics in Computer Science

The convergence of ancient wisdom and modern computation is an intriguing frontier explored in this research paper, which investigates the potential application of Vedic Mathematics in the domain of Computer Science. Vedic Mathematics, originating from ancient Indian texts known as the Vedas, offers a distinct approach to mathematical problem - solving that emphasizes mental agility, pattern recognition, and elegant algorithms. This paper delves into the historical context of Vedic Mathematics, tracing its origins and key principles, and highlights its relevance in contemporary Computer Science. The intersection of Vedic Mathematics and Computer Science is illuminated through the identification of shared principles, such as pattern recognition and optimization. The paper explores specific Vedic Mathematics techniques that have the potential to enhance computational efficiency, including the "Nikhilam" method for subtraction and the "Vertically and Crosswise" method for multiplication. Case studies are presented to demonstrate the practical application of Vedic Mathematics techniques in Computer Science, spanning areas such as data processing, algorithmic optimization, and error detection and correction. However, this integration is not without its challenges and ethical considerations. The paper addresses concerns about accuracy and reliability, particularly when integrating unconventional mathematical techniques into modern computational paradigms. Cultural sensitivity and proper acknowledgment of the origins of Vedic Mathematics are paramount, as is the need to ensure that the techniques are accessible to a diverse audience. The ethical implications of cross - cultural knowledge exchange are also explored, emphasizing the importance of collaboration and respect. Looking ahead, the paper outlines potential research directions and implications for education in Computer Science. The integration of Vedic Mathematics offers opportunities for developing novel algorithms, enhancing problem - solving skills, and fostering cross - cultural understanding. Collaborations between mathematicians and computer scientists are highlighted as a promising avenue for innovation in algorithmic design. In conclusion, the fusion of Vedic Mathematics and Computer Science represents a dynamic interplay of ancient traditions and modern advancements. As we navigate the complexities of integration, we embark on a journey that transcends disciplinary boundaries, fosters cultural appreciation, and holds the potential to reshape computational methodologies in the digital age.

AI-Powered Conflict Resolution: Transforming Virtual Team Dynamics in Real-Time

This paper explores the transformative role of Artificial Intelligence (AI) in conflict resolution within virtual team dynamics. As remote work becomes increasingly prevalent, traditional conflict resolution strategies often fall short in addressing challenges like communication barriers, cultural differences, and workload imbalances. Leveraging AI-powered tools offers novel solutions by providing real-time communication clarity, data-driven insights, and objective mediation. The study emphasizes the potential of AI in fostering team cohesion through enhanced emotional intelligence, sentiment analysis, and automated conflict identification. Furthermore, it examines the ethical considerations, challenges, and future trends of AI integration, highlighting its ability to facilitate personalized, scalable, and proactive conflict resolution strategies. This research underscores AI's capacity to revolutionize virtual team management, enabling productive collaboration in an increasingly digital and globalized work environment.

Preface

As Editor-in-Chief of GenoMed Connect, it is my great pleasure to introduce the inaugural issue of this journal, dedicated to bridging the fields of genomics and medicine. Our vision for GenoMed Connect is to serve as a platform for the latest research that informs precision medicine and promotes integrative healthcare approaches. This inaugural issue features a diverse collection of seven articles, showcasing the journal's commitment to interdisciplinary research and its impact on advancing medical science. Each study addresses a unique aspect of genomics and medical research: 1. Evaluation of IL-12 Gene Expression in Coronary Artery Disease examines the role of IL-12 in atherosclerosis, shedding light on inflammatory pathways that may serve as therapeutic targets in coronary artery disease. 2. A Case Report on Epidermolytic Hyperkeratosis Type 1 introduces a novel mutation in the KRT1 gene, expanding the understanding of genetic dermatological conditions and offering insights into the molecular underpinnings of epidermolytic hyperkeratosis. 3. Identification of Prognostic Biomarkers in Uveal Melanoma explores tumor immune microenvironments, identifying genetic markers that could improve the prediction and treatment strategies for this rare but aggressive form of eye cancer. 4. HLA Class I and II Gene Diversity in North India provides an extensive genetic analysis that is valuable for transplantation immunology and population genetics, enhancing the knowledge of HLA variability and its implications in disease association. 5. Investigation of TLR and CD36 Expression in Coronary Artery Disease delves into the immune response mechanisms in coronary artery disease, examining the role of toll-like receptors and their potential as markers for disease progression. 6. Ethical Approaches in Reporting Secondary Findings from Exome Sequencing reviews current guidelines and ethical considerations, addressing challenges in reporting incidental findings and highlighting pathways for more responsible genomic data sharing. 7. The Role of Microbes in Endocrine Health presents an intriguing review on microbial endocrinology, exploring the influence of the gut microbiome on hormone regulation and its implications for metabolic health. These articles embody the interdisciplinary nature of GenoMed Connect, contributing to a deeper understanding of genomics in disease diagnosis, treatment, and ethical considerations. I extend my gratitude to our dedicated editorial board, the reviewers, and the team at Scifiniti for their hard work in bringing this issue to life. I look forward to witnessing the journal’s growth as it continues to foster collaboration and drive innovation in genomic medicine. I hope that the research presented in this issue inspires readers and advances our collective efforts to improve healthcare through genomic insights.

Context is key: ethical considerations related to consent and study design in acute cardiac care research.

Context is key: ethical considerations related to consent and study design in acute cardiac care research.

Gamified Digital Mental Health Interventions for Young People: Scoping Review of Ethical Aspects During Development and Implementation.

Young people are particularly at risk of developing mental health problems, a challenge exacerbated by the COVID-19 pandemic. Digital tools such as apps and chatbots show promise in providing accessible, cost-effective, and less stigmatized ways of strengthening their mental health. However, while these interventions offer benefits, they extend mental health measures beyond traditional therapeutic settings and relationships, which raises ethical concerns due to the absence of established guidelines and regulations. This is particularly notable for technologies incorporating serious gaming elements. In addition, adolescents are in a sensitive and at times vulnerable phase, which shows great potential for the effective use of preventive and sensitizing mental health measures. Considering the lack of an integration into existing mental health structures among many young users, ethical considerations become crucial. This scoping review aims to build a knowledge base on the ethical aspects of developing and implementing gamified digital mental health interventions for young people. We conducted a search on research articles and conference papers from 2015 to 2023 in English, German, and Spanish. We identified 1815 studies using a unique combination of keywords in the databases Scopus, Web of Science, MEDLINE, and PsycINFO. After removing duplicates (741/1816, 40.8%), we included a total of 38 publications in this review following a double screening process. This review found that ethically relevant aspects were discussed with regard to (1) research ethics, (2) ethical principles (including privacy, accessibility, empowerment and autonomy, cultural and social sensitivity, and co-design), (3) vulnerable groups, and (4) social implications (including implementation using facilitators in specific social contexts, relationship with other therapeutic options, economic aspects, and social embeddedness of technologies). This scoping review identified a prevailing limited interpretation of "ethics" as research ethics across the included publications. It also shows a lack of discussion on the social embeddedness of technologies and that co-design is frequently viewed in instrumental terms and vulnerability is mostly addressed pragmatically. Through providing concrete examples of how mental health researchers and game designers thus far have addressed and mitigated ethical challenges in specific interventions, this review illustrates how ethical issues do or do not prompt diverse reflections, mitigation strategies, and actions. It advocates for ethics to be integrated as an ongoing practice throughout all stages of developing and implementing serious game elements in mental health interventions for young people.

“Meet me at the backdoor”: A multiple case study of academic entrepreneurs bypassing their technology transfer offices

This article investigates the underexplored phenomenon of technology transfer office (TTO) bypassing in academic entrepreneurship. While TTOs are established to centralize and support intellectual property-based commercialization, a significant portion of entrepreneurial projects avoid the TTO. Relying on both economic (transaction cost theory) and ethical (Tyler's justice model) considerations, this study explores the motivations and contextual factors behind a researcher's decision to commercialize an invention using means other than the TTO. This multiple case study employs an in-depth exploratory qualitative approach to investigate five academic entrepreneurs across different disciplines in Canadian universities who chose to bypass their TTO, often in contravention of institutional policies. Our findings reveal a complex interplay among individual motivations, institutional policies, and market realities. We identified four paths of awareness and strategic intent in this process ranging from unintentional non-compliance to tactical avoidance which challenge a simplistic perception of TTO bypassing as merely unintentional rather than deliberate. The study also reveals four overlapping contexts that promote TTO bypassing: confidence in personal expertise, previous negative experience of using the TTO, peer-influenced skepticism, and external partner challenges. Furthermore, the findings show that the reasons for bypassing include both economic and ethical motivations which steer academic entrepreneurs toward alternative, privately managed commercialization paths. The article concludes with some implications for university managers and policymakers related to how to address the multifaceted motivations for TTO bypassing.

Application of Machine Learning Models in Social Sciences: Managing Nonlinear Relationships

The increasing complexity of social science data and phenomena necessitates using advanced analytical techniques to capture nonlinear relationships that traditional linear models often overlook. This chapter explores the application of machine learning (ML) models in social science research, focusing on their ability to manage nonlinear interactions in multidimensional datasets. Nonlinear relationships are central to understanding social behaviors, socioeconomic factors, and psychological processes. Machine learning models, including decision trees, neural networks, random forests, and support vector machines, provide a flexible framework for capturing these intricate patterns. The chapter begins by examining the limitations of linear models and introduces essential machine learning techniques suited for nonlinear modeling. A discussion follows on how these models automatically detect interactions and threshold effects, offering superior predictive power and robustness against noise compared to traditional methods. The chapter also covers the practical challenges of model evaluation, validation, and handling imbalanced data, emphasizing cross-validation and performance metrics tailored to the nuances of social science datasets. Practical recommendations are offered to researchers, highlighting the balance between predictive accuracy and model interpretability, ethical considerations, and best practices for communicating results to diverse stakeholders. This chapter demonstrates that while machine learning models provide robust solutions for modeling nonlinear relationships, their successful application in social sciences requires careful attention to data quality, model selection, validation, and ethical considerations. Machine learning holds transformative potential for understanding complex social phenomena and informing data-driven psychology, sociology, and political science policy-making.