Ethical Considerations Research Articles

Ethical considerations in researching marginalised voices: a focus on women and gender-diverse individuals in Central American prison

ABSTRACT This paper explores the ethical complexities inherent in researching marginalised voices within Central American prisons, specifically focusing on women and gender-diverse individuals presumed to be affiliated with armed groups. Drawing on extensive fieldwork conducted across Central America, the paper explores the challenges researchers face when navigating carceral spaces dominated by gang violence and informal governance. It highlights the need for addressing the ethical dilemmas that arise when engaging with incarcerated individuals. By highlighting the importance of reflexivity, recognition of gatekeepers, ensuring the safety of incarcerated individuals, sensitivity to vulnerabilities and diversity, and fostering empathy, the authors contribute to the broader discourse on research ethics and methodology in high-risk environments. This study casts a spotlight on the ethics of researching prisons in Central America, underscoring the urgency of ethical research practices that prioritise the dignity, agency, and humanity of research subjects.

Employing artificial intelligence technology in developing practical content for media specialization—A case study of Palestine Technical University, Kadoorie

This study investigates the utilization of artificial intelligence (AI) technology to enhance practical content development within the media specialization program at Palestine Technical University, Kadoorie. The primary objective is to examine the extent to which media specialty lecturers employ AI technology in developing practical content. A mixed-methods approach is employed, qualitative data are gathered through in-depth interviews with faculty members to elucidate their perceptions and experiences regarding the integration of AI technology in practical content development. The study aims to provide valuable insights into the benefits and challenges of AI integration in practical content development for media specialization programs The study reveals diverse views on AI integration in media education at Palestine Technical University, Kadoorie. Faculty recognize AI’s benefits like personalized learning and productivity but also express concerns about over-reliance and ethics. Consensus exists on cautious AI implementation to maximize benefits and address drawbacks. Obstacles to AI adoption include cost, skills gaps, and ethical considerations, highlighting the complexity of integration. The study emphasizes a balanced approach, offering insights for enhancing practical content development in media specialization programs at Palestine Technical University, Kadoorie.

Towards a Clinically Relevant Appreciation of the Cost of Caring: A Study of Palliative Care Physicians in Malaysia.

Background: The cost of caring for patients and their families in the midst of interconnected resource, ethical, moral, legal and practical considerations compromises a physician's emotional and physical well-being and therefore patient care. Whilst the cost of caring is historically best associated with compassion fatigue, data has suggested that this may extend to other related concepts, such as vicarious trauma, secondary traumatic stress and burnout. In particular, palliative care physicians are especially vulnerable as they witness and encounter more cases of death and dying. Methods: This study aims to provide a more clinically relevant notion of the cost of caring amongst palliative care physicians in Malaysia. 11 physicians underwent semi-structured interviews as part of the Systematic Evidence-Based Approach (SEBA) for prospective studies. Results: Analysis of the interview transcripts revealed the following domains: (1) conceiving the costs of caring; (2) risk factors; and (3) support mechanisms. Conclusion: This SEBA-guided study into the cost of caring amongst Malaysian palliative care physicians suggests that the costs of caring extend beyond encapsulating moral distress, compassion fatigue, vicarious trauma, secondary traumatic stress and burnout. Rather, the data suggests a personalized notion that varies with individual and contextual factors which are in flux and change over time. A longitudinal, personalized and holistic mentoring program is therefore proposed to counter this cost.

Exploring the Impact of Artificial Intelligence on Healthcare Management: A Combined Systematic Review and Machine-Learning Approach

The integration of artificial intelligence (AI) in healthcare management marks a significant advance in technological innovation, promising transformative effects on healthcare processes, patient care, and the efficacy of emergency responses. The scientific novelty of the study lies in its integrated approach, combining systematic review and predictive algorithms to provide a comprehensive understanding of AI’s role in improving healthcare management across different contexts. Covering the period between 2019 and 2023, which includes the global challenges posed by the COVID-19 pandemic, this research investigates the operational, strategic, and emergency response implications of AI adoption in the healthcare sector. It further examines how the impact of AI varies across temporal and geographical contexts. The study addresses two main research objectives: to explore how AI influences healthcare management in operational, strategic, and emergency response domains, and to identify variations in the impact of AI on healthcare management based on temporal and geographical contexts. Utilizing an integrated approach, we compared various prediction algorithms, including logistic regression, and interpreted the results through SHAP (SHapley Additive exPlanations) analysis. The findings reveal five key thematic areas: AI’s role in enhancing quality assurance, resource management, technological innovation, security, and the healthcare response to the COVID-19 pandemic. The study highlights AI’s positive influence on operational efficiency and strategic decision making, while also identifying challenges related to data privacy, ethical considerations, and the need for ongoing technological integration. These insights provide opportunities for targeted interventions to optimize AI’s impact in current and future healthcare landscapes. In conclusion, this work contributes to a deeper understanding of the role of AI in healthcare management and provides insights for policymakers, healthcare professionals, and researchers, offering a roadmap for addressing both the opportunities and challenges posed by AI integration in the healthcare sector.

Field Notes from the Border: Lessons Learned in Conducting Mental Health Research Involving Newly Arrived Latinx Immigrants as Study Participants.

In this paper, we describe a research protocol for surveying and interviewing Latinx immigrants recently arrived at the US southern border, and we raise important and unique issues that need to be considered with this population. The main objective is to share experiences, challenges, opportunities, and essential considerations (which we call lessons learned) that researchers should take into account when working with this vulnerable study population. The six lessons learned focus on: (1) fostering relationships with community partners; (2) participant consent and compensation; (3) linguistic and cultural fluency of researchers; (4) adapting data collection procedures to the environment and conditions; (5) establishing trust with participants and being trustworthy; and (6) addressing the ethical considerations of research with immigrant populations and the positionality of researchers. This paper provides a unique perspective of working with a vulnerable population that is in transit, often coming from circumstances of danger and risk to their lives, who are now headed towards new and uncertain experiences that may include disadvantage, exclusion or other risks. The lessons learned from the field inform best practices for working with recently arrived Latinx immigrants, with implications for public health research that may extend to other immigrant populations.

Merging Realities for Better Health: A Dive Into Virtual Healthcare

This paper delves into the transformative use of Virtual Reality in the public health domain, focusing on its psychological effects, the effectiveness of VR-based patient education interventions, and ethical considerations surrounding the use of personal data and privacy. Through the examination of peer-reviewed studies, the research found that VR offers innovative solutions on health education and mental health outcomes. The immersive digital environment of VR offers breakthroughs in delivering patient education and psychological interventions. As VR devices continuously collect vast amounts of sensitive user information to create unique experiences, the paper discusses the need for data protection and ethical guidelines to protect user privacy and gain consumer trust. However, it is important to address the significant ethical and social issues, and adverse reactions, such as cyber-sickness or escapism, to fully realize the benefits of Virtual Reality.

Electroencephalography-Based Brain-Computer Interfaces in Rehabilitation: A Bibliometric Analysis (2013–2023)

EEG-based Brain-Computer Interfaces (BCIs) have gained significant attention in rehabilitation due to their non-invasive, accessible ability to capture brain activity and restore neurological functions in patients with conditions such as stroke and spinal cord injuries. This study offers a comprehensive bibliometric analysis of global EEG-based BCI research in rehabilitation from 2013 to 2023. It focuses on primary research and review articles addressing technological innovations, effectiveness, and system advancements in clinical rehabilitation. Data were sourced from databases like Web of Science, and bibliometric tools (bibliometrix R) were used to analyze publication trends, geographic distribution, keyword co-occurrences, and collaboration networks. The results reveal a rapid increase in EEG-BCI research, peaking in 2022, with a primary focus on motor and sensory rehabilitation. EEG remains the most commonly used method, with significant contributions from Asia, Europe, and North America. Additionally, there is growing interest in applying BCIs to mental health, as well as integrating artificial intelligence (AI), particularly machine learning, to enhance system accuracy and adaptability. However, challenges remain, such as system inefficiencies and slow learning curves. These could be addressed by incorporating multi-modal approaches and advanced neuroimaging technologies. Further research is needed to validate the applicability of EEG-BCI advancements in both cognitive and motor rehabilitation, especially considering the high global prevalence of cerebrovascular diseases. To advance the field, expanding global participation, particularly in underrepresented regions like Latin America, is essential. Improving system efficiency through multi-modal approaches and AI integration is also critical. Ethical considerations, including data privacy, transparency, and equitable access to BCI technologies, must be prioritized to ensure the inclusive development and use of these technologies across diverse socioeconomic groups.

INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE

This study examines the application of machine learning algorithms to enhance financial inclusion in microfinance, focusing on credit scoring, risk and fraud detection, and customer segmentation. We performed feature engineering and employed models such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), Support Vector Machines (SVM), Autoencoders, Isolation Forests, and K-means Clustering. LightGBM achieved the highest accuracy (89.6%) and AUC (0.92) in credit scoring, while Random Forests demonstrated strong performance in both loan approval (86.7% accuracy) and fraud detection (87.6% accuracy, AUC of 0.88). SVM also performed competitively, and unsupervised methods like Autoencoders and Isolation Forests showed potential for anomaly detection but required further refinement.K-means Clustering excelled in customer segmentation with a silhouette score of 0.72, enabling tailored services based on client demographics. Our findings highlight the significant impact of machine learning on improving credit scoring accuracy, reducing fraud risks, and enhancing customer service delivery in microfinance, thereby promoting financial inclusion for underserved populations. Ethical considerations and model interpretability are crucial, particularly for smaller institutions. This study advocates for the broader adoption of machine learning in the microfinance sector.

Modern Approaches for Improved Healthcare Access and Efficiency: A Critical Analysis

The rapid advancement of technology and the evolving needs of healthcare systems have spurred the development of innovative approaches aimed at improving healthcare access and efficiency. This article critically analyzes modern methods, including telemedicine, artificial intelligence (AI), electronic health records (EHR), and integrated care models, exploring their effectiveness, challenges, and impact on stakeholders. Through a balanced examination of case studies, this study highlights both successful implementations and areas of concern, such as disparities in digital accessibility, data security, and system interoperability. The findings underscore the transformative potential of these approaches while calling attention to the regulatory and ethical considerations that accompany them. Recommendations for future strategies emphasize policy development, technology enhancements, and global collaboration to ensure equitable and efficient healthcare access. This analysis offers healthcare practitioners, policymakers, and researchers insights into optimizing these modern solutions while mitigating risks, ultimately fostering a more resilient and inclusive healthcare system.

Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective.

To provide practical insights that delve into the ethical issues and regulatory implications of generative artificial intelligence (GenAI) in healthcare. Ethical Challenges and Regulatory Impact in China is used as an example. Despite China's efforts to strike a delicate balance between protecting public welfare and promoting technological advancement, numerous unresolved issues persist in the practical integration of generative artificial intelligence into healthcare settings. Key issues such as data application, privacy protection, cost-effectiveness and regulatory remain areas of ambiguity that require clarification. Stringent ethical guidelines, data privacy protection measures and continuous supervision and evaluation of artificial intelligence decisions will help enhance the expected benefits of GenAI in healthcare. The potential use of GenAI in healthcare has garnered widespread attention, emerging as a significant global research topic. However, its application in this domain presents substantial ethical and regulatory challenges. Compared to other fields, GenAI's role in healthcare is more sensitive and complex, necessitating an urgent assessment of its ethical implications for future development and deployment. Challenges and ethical considerations are particularly pronounced in developing countries with limited healthcare resources.

Examining the ethical and sustainability challenges of legal education’s AI revolution

ABSTRACT Two of the forces that have most shaped the landscape in higher education over the last few years have been sustainability on one hand and Artificial Intelligence (AI) on the other. Bringing these phenomena together, this article examines the multifaceted sustainability challenges posed by integrating AI into legal education. The integration of AI into legal education has promised to revolutionise the field, offering unprecedented opportunities for efficiency and innovation. However, this transformative shift is accompanied by ethical and sustainability challenges. This article explores important sustainability issues arising from AI adoption in legal education, emphasising equity, ethics and the long-term viability of AI-driven initiatives. Strategies to promote fairness, inclusivity and ethical AI practices are explored, alongside resource allocation schemes, digital divide mitigation and sustainable faculty training programmes. Ethical considerations are examined, focusing on AI biases and policies for promoting equity and inclusion. This article also discusses the challenge of regulatory compliance in an evolving AI landscape. The intersection of AI adoption, sustainability ethics and legal education necessitates a conscientious integration of sustainability principles to navigate ethical dilemmas responsibly. By addressing these challenges, legal education can lead the way in shaping a future where AI serves as a valuable tool.

AI-Powered Search Systems : Integrating Machine Learning with Search Technology for High-Scalability Applications

This article comprehensively analyzes integrating Artificial Intelligence (AI) and machine learning techniques into high-scalability search systems. We explore AI-powered search's theoretical foundations and practical implementations, focusing on advanced ranking algorithms, natural language processing for query understanding, and optimized distributed architectures. We demonstrate significant improvements in search relevance and efficiency through experiments conducted on a large-scale dataset comprising 100 million web pages and 1 million real-world queries. Our AI-powered system showed a 15% increase in Normalized Discounted Cumulative Gain (NDCG) for complex queries and a 12% improvement in Mean Reciprocal Rank (MRR) for navigational queries compared to traditional keyword-based approaches. We also address critical challenges in maintaining system scalability and performance, including data synchronization, real-time model updates, and resource management in distributed environments. The article further discusses emerging trends, such as graph neural networks and multimodal search capabilities, alongside ethical considerations and data privacy concerns. Our findings provide valuable insights for researchers and practitioners aiming to develop next-generation search platforms capable of handling the increasing complexity and volume of digital information while ensuring responsible AI integration.

Telesurgery: humanitarian and surgical benefits while navigating technologic and administrative challenges.

Telesurgery, the remote execution of surgical procedures through telecommunication and robotic systems, has witnessed substantial growth in recent years, promising to address global healthcare disparities and enhance surgical expertise. This paper explores the humanitarian and surgical benefits of telesurgery, emphasizing its potential to provide expert surgical care to underserved regions. Despite its transformative potential, telesurgery faces significant technologic challenges, including issues of data transmission, latency, and the need for advanced robotic platforms. The advent of 5G networks and innovative robotic systems provides a promising technological landscape, yet global disparities in 5G coverage remain a concern. Ethical considerations, ranging from preserving the surgeon-patient relationship to addressing patient vulnerability and conflicts of interest, are pivotal aspects that demand attention. The paper underscores the importance of clear regulatory frameworks and international collaboration to navigate legal complexities and ensure ethical standards. As telesurgery progresses, integrating artificial intelligence, augmented reality, and haptic feedback technologies holds promise for further advancements. Despite these challenges, telesurgery has the potential to achieve equitable access to expert surgical care; however, it requires a collective effort to overcome its intricate technologic and administrative hurdles.

No Conflicting Loyalties in Parents When Their Healthy Child Donates Stem Cells to a Severely Ill Sibling: An Interview Study.

Background: When a potential stem cell donor to a seriously ill child is a healthy sibling below 18 years, Swedish parents have the legal right and obligation to decide on behalf of the donor child. However, there are potentially conflicting loyalties when parents have one severely ill child in need for a cure and one healthy child who will be subjected to medical procedures. This study explored parents' experiences related to their decision on stem cell donation, as well as ethical considerations in the donation process where outcomes are uncertain. Method: Individual interviews were performed with 18 parents of 13 minor donors after successful hematopoietic stem cell transplantations. Interviews were analyzed using inductive Reflexive Thematic Analysis. Results: The parents were living with the threat of losing a child, and in this context, the main theme No conflicting loyalties was found and included four subthemes; Focus on the ill child, Sibling as the preferred donor, Obvious that the healthy child should donate, and Keep on keeping on. Conclusion: When a healthy child is a potential donor to an ill sibling, their parents' main focus is on the cure for the ill child. The lack of obvious conflicting loyalties among parents highlights the need to secure an ethical process for healthy minor donors and the importance of a separate donor advocate for these minor donors.

Machine Learning Meets Cybersecurity

In an era where digital transformation is accelerating, the integration of machine learning (ML) into cybersecurity is becoming increasingly crucial. This article delves into the significant role that ML plays in enhancing cybersecurity measures. ML's ability to process and analyze vast amounts of data at unprecedented speeds allows for more efficient threat detection, anomaly identification, and predictive analytics. These capabilities are essential in pre-empting and mitigating cyber threats in real-time. However, the same technologies that bolster defences are also being leveraged by cybercriminals to develop more sophisticated attacks, posing unique challenges. This article explores the dual-edged nature of ML in cybersecurity, highlighting both its benefits and the potential risks. It discusses the challenges of bias in ML algorithms, the vulnerabilities exposed through adversarial attacks, and the ethical and privacy considerations that come with deploying ML solutions. Furthermore, it provides insights into the future trends and developments in ML-driven cybersecurity and underscores the importance of synergizing human expertise with machine intelligence to build more resilient cybersecurity frameworks. Through this exploration, the article aims to provide a comprehensive understanding of the transformative impact of ML on cybersecurity and the path forward in addressing the accompanying challenges.

Financial Incentives for Improving Maternal Health: A Review of Ethical Considerations

Abstract Maternal health is considered a key global priority by the World Health Organization, and several strategies are used to promote it. Especially in lower- and lower-middle-income countries, one widely used strategy employs financial incentives to motivate pregnant women to access available healthcare. While such interventions have been extensively empirically evaluated, their normative aspects appear to have received less attention. To address this gap, we systematically searched and reviewed normative and qualitative literature to map and analyze the ethical considerations of using financial incentives to promote maternal health. Twenty-three articles (four normative and 19 qualitative) were included in the review. We grouped ethical considerations under six categories, namely the program structure, promoting institutional births, of the ethical significance of cash, justifying conditionalities for maternal health, effects on individuals’ decision-making and the experiences of vulnerable groups. While incentives could be used for beneficent goals, we show how they may emphasize global rather than local solutions, impose unfamiliar sociocultural values, unfairly expect behavior change and disempower individuals and groups. We argue that future maternal health policy, especially those targeting vulnerable groups and minorities, would benefit from taking these normative considerations into account.

Conceptual models of the sociotechnical: Introduction to special issue

AbstractThis special issue of the “Journal of the Association for Information Science and Technology” examines conceptual models as products of, and tools for, critical inquiry in Information Science (IS). The papers included in this issue present diverse perspectives on how conceptual models impact sociotechnical systems, spanning topics such as knowledge organization, representation, and information system design. Key themes include the intersection of model development with ethical considerations, the historical and future implications of conceptual modeling decisions, and the potential for conceptual models to address issues of power, representation, and justice in emerging technologies. This introduction situates the contributions within broader discussions of conceptual modeling in IS and highlights the field's unique approach to reflexive critique and sociotechnical analysis.

The impact of big data on decision‐making, processes and organizational change: An essay of synthesis

AbstractThis special issue looks at how big data affects business decisions, processes, and organizational change within organizations. The issue starts with a review of the latest research in the field, including key developments and ongoing debates. The literature review shows how Big Data is affecting how organizations work, including ethical issues, internal rules, and using new technology. Next, the issue presents three key papers on how Big Data affects modern organizations. The first paper looks at how Big Data how Big Data is helping to make cities smarter. The third paper looks at how new technologies like artificial intelligence, blockchain, and quantum computing affect financial organizations. Together, these contributions show the need to balance innovation with risk management. They advocate for ethical considerations and policy frameworks as organizations navigate the complexities of the Big Data era. This essay of synthesis, from literature review to focused studies on decision‐making, operations, and organizational change, provides a holistic understanding of the role of Big Data in shaping the future of business.

A Review on Mixed Reality Operating Systems : Current Trends, Challenges and Prospects

This comprehensive article explores the rapidly evolving landscape of Mixed Reality (MR) operating systems, examining their core functionalities, architectural components, and the cutting-edge trends shaping their development. The article provides an in-depth analysis of the challenges facing MR OS developers, including interoperability, scalability, and user experience optimization, while offering insights into potential solutions. A comparative article of leading MR platforms highlights the diverse approaches and capabilities within the current ecosystem. The article delves into future prospects, discussing the integration of artificial intelligence, advanced user interaction paradigms, and their potential impact across various industries. Additionally, it addresses strategies for overcoming current limitations and explores the broader implications of MR technology on society, including ethical considerations and privacy concerns. By synthesizing current research and industry developments, this paper offers a comprehensive overview of the state of MR operating systems, their potential applications, and the transformative role they are poised to play in shaping human-computer interaction and digital experiences. The article concludes by emphasizing the critical importance of continued innovation in MR operating systems for advancing the field and realizing the full potential of mixed reality technology in both personal and professional domains.

Assessing the Impact of Generative Pre-Trained Transformers on AI Literacy and Public Awareness in India

Generative Pre-Trained Transformers (GPTs), a class of Artificial Intelligence (AI) models capable of producing human-like responses, have become widely adopted since their release. In India, this development raises questions about whether GPTs have influenced public awareness and interest in AI. Understanding this influence is crucial, particularly in the context of AI literacy. This study examines the impact of GPT adoption on creating increased awareness and public interest in AI in India. AI literacy is assessed using the four-factor framework proposed by Ng et al., (2021): knowledge and understanding of AI, uses and applications, evaluation and creation of new solutions, and ethical considerations. Information-seeking behavior serves as the theoretical foundation for examining changes in public interest across these dimensions. Google search volumes from 2020 to 2024 are analyzed to measure public engagement, with data divided into two periods: 2.5 years prior to and 1.5 years following the initial public release of ChatGPT in November 2022. Welch’s t-test is applied to assess changes in search volumes across these time periods. The results indicate a statistically significant increase in interest across all four aspects of AI literacy. The adoption of GPTs has significantly boosted public engagement with AI literacy in India, enhancing awareness and interest across all facets of AI knowledge and application.