Ethical Guidelines Research Articles

Acceptance Factors of Generative AI in EFL Teaching: A Pedagogical Perspective

<p>The emergence of generative artificial intelligence (GenAI) has brought abundant potentials and challenges to the educational sector. This study explores the acceptance and adoption of GenAI in English language and its pedagogical affordability for EFL instructors in tertiary level institutions in the Kingdom of Saudi Arabia. An exploratory sequential mixed method was utilized in this study to gain in-depth insights into the quantitative findings. The first quantitative phase recruited 256 EFL instructors in Saudi Arabia while the follow-up qualitative phase incorporated 17 interviewees. The findings of this study indicate that anthropomorphism, trust, ethics and regulations, and pedagogical affordability are significant determinants of instructors’ acceptance of GenAI. Conversely, instructors’ acceptance was not significantly influenced by communication capability of GenAI. Yet, there was no evidence of significant differences in GenAI acceptance or adoption due to demographic variables such as gender, age, and degree. The pedagogical affordances of GenAI appears to be the most acceptance factor specifically the productivity and efficiency that GenAI afford for instructors. The study recommends establishing ethical guidelines and embracing transparency and accountability to maintain academic integrity.</p>

Obstacles to the Full Realization and Adoption of Artificial Intelligence (AI)

Artificial Intelligence (AI) holds transformative potential across various sectors, yet its journey to full realization and widespread adoption is impeded by several significant obstacles. These challenges span technical, ethical, societal, research and development, implementation, and regulatory domains, necessitating a comprehensive and multi-faceted approach to address them.Technical Challenges: AI systems require vast amounts of high-quality, labeled data, which is often unavailable or of poor quality. AI models also struggle to generalize beyond specific tasks, and their opaque decision-making processes hinder trust. Scaling AI to handle large datasets and interactions, while ensuring robustness and security against adversarial attacks, poses major technical hurdles.Ethical and Societal Challenges: AI can perpetuate and amplify biases, leading to unfair outcomes and privacy issues. AI-driven automation risks job displacement, necessitating measures for worker support and retraining. The rapid development of AI outstrips regulatory bodies' abilities to create appropriate frameworks, complicating responsible deployment.Research and Development Challenges: AI struggles to integrate information from different modalities and lacks human-like common sense and reasoning. Improving AI's learning efficiency to reduce its dependence on vast data is a significant research focus.Implementation Challenges: Integrating AI into legacy systems, addressing the talent shortage, and managing the high costs of AI development and deployment are substantial barriers.Ethical and Regulatory Uncertainty: The absence of universally accepted ethical guidelines and varying regulatory landscapes create uncertainty and potential misuse.Addressing these multifaceted obstacles requires a concerted effort from researchers, developers, policymakers, and society to create robust, fair, and transparent AI systems, unlocking AI's full potential and societal benefits.

Investigating Adoption Determinants, Obstacles, and Interventions for AI Implementation in Emirati Media Organizations

This research explores the integration and impact of artificial intelligence (AI) within Emirati media organizations, focusing on adoption determinants, obstacles, and interventions. Utilizing a mixed-methods approach, the study incorporates qualitative data from 21 in-depth interviews with media professionals and a thematic analysis of 30 scholarly articles published between 2019 and 2024. The research aims to provide a nuanced understanding of AI's transformative effects on the UAE media landscape. Key findings reveal that AI is significantly enhancing content creation, distribution, and audience engagement processes within Emirati media organizations. AI technologies such as machine learning, natural language processing, and predictive analytics are being leveraged to improve operational efficiency, streamline workflows, and deliver personalized content to audiences. These advancements are driving competitive advantages and elevating the overall quality of media output. Despite these benefits, several challenges hinder widespread AI adoption. Technological barriers, including limited access to advanced AI tools and infrastructure, pose significant obstacles. Additionally, there is a pronounced shortage of skilled personnel capable of implementing and managing AI systems, which exacerbates concerns over job displacement and workforce anxiety. Ethical considerations, such as ensuring unbiased AI algorithms and protecting user privacy, are also critical issues that require attention. The study underscores the importance of continuous training and upskilling for media professionals to adapt to the evolving technological landscape. It highlights the role of robust ethical guidelines and governance frameworks in promoting responsible AI use. Government support, through targeted policies, funding initiatives, and incentives, is identified as a crucial factor in facilitating AI integration and addressing adoption barriers. The research concludes by providing strategic recommendations for media organizations and policymakers. It advocates for the development of comprehensive AI adoption roadmaps that include capacity-building initiatives, ethical oversight, and collaborative efforts between industry stakeholders. By addressing these areas, Emirati media organizations can harness the full potential of AI to drive innovation, improve audience engagement, and maintain competitiveness in a rapidly digitizing global media environment. This study contributes to the growing body of knowledge on AI in media, offering valuable insights into the specific context of the UAE. It provides a foundation for future research on AI's role in media and serves as a guide for organizations seeking to navigate the complexities of AI adoption.

EXPRESS: AI-based Financial Advice: An Ethical Discourse on AI-based Financial Advice and Ethical Reflection Framework

This article presents a first step in identifying the ethical issues of AI-based financial advice. Consumers must navigate an ever more complex array of financial decisions. (Generative) AI-based financial advice may increase access to and acceptance of financial advice and strengthen consumers’ financial well-being. However, significant ethical challenges exist in designing, developing, and deploying AI-based financial advice. To analyze the perils and pitfalls of AI-based financial advice, we develop a definition of what constitutes good AI-based financial advice and provide a first assessment of ethical challenges related to AI-based financial advice. The iterative multi-stakeholder approach, including workshops and semi-structured interviews with consumers and experts, results in an ethical discourse structured around the four fundamental values of the European Commission’s Ethics Guidelines for Trustworthy AI, human autonomy, explicability, fairness, prevention of harm, and trust as the overall objectives. Based on our analyses, we derive a simple yet comprehensive AI Ethics Framework for Financial Advice (AIFA). The AIFA reflection framework guides public policymakers, managers of financial service providers, and technology developers in incorporating ethical discourse in developing and deploying (generative) AI-based financial advice.

A Systematic Review to Explore the Role of Industry and Regulators on Minimally Invasive Aesthetics Education: A Tale of Two Cities.

The aesthetic medicine industry has seen significant growth due to technological advancements and increasing demand for minimally invasive procedures. This sector's influence on educational institutions is profound, often shaping curricula to align with industry standards. However, this focus can sometimes narrow educational scope, potentially compromising the broader intellectual and ethical development of medical professionals. This study employs a comprehensive literature review to examine the influence of the aesthetic medicine industry on medical education. A systematic search was conducted across academic databases including PubMed, Google Scholar, and ScienceDirect, focusing on articles published between January 2003 and December 2023. Inclusion criteria encompassed studies examining the impact of industry on medical education, financial relationships, ethical implications, and student outcomes. Out of 279 studies identified, 21 met the inclusion criteria. The analysis highlighted themes such as the need for a balanced curriculum integrating both theoretical and practical training, the impact of financial incentives on educational integrity, and the importance of ethical guidelines. The literature underscores the necessity for standardised curricula and enhanced regulatory oversight to ensure comprehensive and unbiased education. The aesthetic medicine industry's significant influence on medical education necessitates a balanced approach that integrates industry advancements with ethical and comprehensive educational practices. Addressing these challenges requires collaboration between academic institutions and industry partners, alongside stringent regulatory oversight. By ensuring educational integrity and fostering holistic training, the field can produce well-rounded professionals capable of ethical and effective practice in aesthetic medicine. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Smart Medicine: The Role of Artificial Intelligence and Machine Learning in Next-Generation Healthcare Innovation

The paper focuses on big data and healthcare pharmaceuticals, which pose both great promise and challenge as they can contribute to well-being and deliver innovation and knowledge to next-generation healthcare products. This paper provides an overview of the benefits that machine learning and big data technology contribute to healthcare systems. Realities such as patient and healthcare professional demands, the number of diseases, insufficient drugs, regulations, legal and ethical guideline restrictions, modus operandi, and technology development take place in the healthcare sector. In addition, big data opportunities and challenges for healthcare companies, research, and healthcare infrastructure are discussed. Finally, a vision of health innovation through personalized medicines as well as smart medicine is presented as a step closer to patients through regulatory, legal, and ethical guidelines, applications, and opportunities that need to be regulated and resolved.

Study protocol of the planetary health action survey PACE: a serial cross-sectional survey to assess the readiness to act against climate change

IntroductionClimate change is a paramount global health threat with multifaceted implications. Societal change is required to mitigate the negative effects of climate change, as well as help people adapt to...

Next generation tissue biobanking: quality assured, financed, and integrated?

Biobanks are essential for biomedical research, particularly in the era of personalized medicine. In Germany, 36biobanks have been established over the past decade that are connected under the German Biobank Alliance (GBA). These biobanks store high-quality biological samples along with clinical data to support research projects. Biobanks can be integrated, handling both tissue and liquid samples, or set up as separate entities depending on specific requirements.Tissue biobanking is especially complex due to the invasive nature of tissue collection and the non-replicability of the samples. Close collaboration between clinics, pathologists, IT specialists, and biobank managers is crucial to ensure the quality of samples and promote interdisciplinary research.The integration of pathology and biobanking is key, both organizationally and technically. Shared IT systems, standardized protocols, and collaborative governance structures are vital for efficient data management. Quality assurance, ethical guidelines, and data protection are critical to maintaining public trust and legal compliance.Long-term financial models are needed to ensure the sustainability of biobanks. The GBA supports emerging biobanks through its "Starterkit" initiative, offering guidance and best practices to help new biobanks develop.Tissue biobanks are indispensable for advancing the understanding of diseases and developing new therapies. However, they must adhere to strict ethical and legal standards to maximize their scientific and societal value.

Digital Due Process : Evaluating the Ethical Implications of Cloud-Based eDiscovery Tools in Criminal Investigations

This article examines the growing role of cloud-based eDiscovery in law enforcement and its implications for investigative efficiency and individual privacy rights. As cloud technologies revolutionize legal data processing, enabling real-time analysis of vast data volumes, law enforcement agencies are experiencing unprecedented acceleration in investigations and improvements in legal compliance. However, this technological leap raises significant privacy concerns, particularly regarding access to personal digital footprints such as emails, social media posts, and linked content. The article explores the delicate balance between leveraging cloud-based eDiscovery for enhanced law enforcement capabilities and safeguarding civil liberties in the digital age. Through analysis of case studies, legal frameworks, and emerging ethical guidelines, we evaluate current challenges and propose solutions for responsible implementation of cloud eDiscovery in law enforcement. The article underscores the urgent need for a comprehensive approach that harmonizes technological advancements with robust privacy protections, ensuring that the pursuit of justice does not come at the cost of individual rights in an increasingly digital world.

The Ethics of AI in the Administration of Higher Education

The rising presence of artificial intelligence (AI) in higher education carries great promise and peril. This paper will examine the ethics of using AI for the operation of these institutions. Key areas of focus include data privacy, bias in decision making, accountability, and the impact on academic integrity. So is the importance of transparency, stakeholder involvement, and the need for ethical guidelines. The paper closes with suggestions for the responsible adoption of AI systems in higher education.

Development of an AI Moral Education Assessment Tool: Identifying and Validating Key Factors through Factor Analysis

This research identified factors contributing to the development of an effective moral education evaluation tool in the context of Artificial Intelligence by using Factor Analysis By exploring these factors , the study seeks to enhance the quality of moral education and provide valuable insights for education administrators.Three hundred respondents who were actively involved in moral education programs at Yichun University in China were selected as participants in this study.Based on the factor analysis, there are five (5) factors that were extracted in the development of moral education and these are: training and support, ethical principles and guidelines, educational innovation, educational technological advancement, and acceptability and adaptability.

Evaluating the User Experience and Usability of the MINI Robot for Elderly Adults with Mild Dementia and Mild Cognitive Impairment: Insights and Recommendations

Introduction: In recent years, the integration of robotic systems into various aspects of daily life has become increasingly common. As these technologies continue to advance, ensuring user-friendly interfaces and seamless interactions becomes more essential. For social robots to genuinely provide lasting value to humans, a favourable user experience (UX) emerges as an essential prerequisite. This article aimed to evaluate the usability of the MINI robot, highlighting its strengths and areas for improvement based on user feedback and performance. Materials and Methods: In a controlled lab setting, a mixed-method qualitative study was conducted with ten individuals aged 65 and above diagnosed with mild dementia (MD) and mild cognitive impairment (MCI). Participants engaged in individual MINI robot interaction sessions, completing cognitive tasks as per written instructions. Video and audio recordings documented interactions, while post-session System Usability Scale (SUS) questionnaires quantified usability perception. Ethical guidelines were followed, ensuring informed consent, and the data underwent qualitative and quantitative analyses, contributing insights into the MINI robot’s usability for this demographic. Results: The study addresses the ongoing challenges that tasks present, especially for MD individuals, emphasizing the importance of user support. Most tasks require both verbal and physical interactions, indicating that MD individuals face challenges when switching response methods within subtasks. These complexities originate from the selection and use of response methods, including difficulties with voice recognition, tablet touch, and tactile sensors. These challenges persist across tasks, with individuals with MD struggling to comprehend task instructions and provide correct answers and individuals with MCI struggling to use response devices, often due to the limitations of the robot’s speech recognition. Technical shortcomings have been identified. The results of the SUS indicate positive perceptions, although there are lower ratings for instructor assistance and pre-use learning. The average SUS score of 68.3 places device usability in the “good” category. Conclusions: Our study examines the usability of the MINI robot, revealing strengths in quick learning, simple system and operation, and integration of features, while also highlighting areas for improvement. Careful design and modifications are essential for meaningful engagement with people with dementia. The robot could better benefit people with MD and MCI if clear, detailed instructions and instructor assistance were available.

Ethics dumping in artificial intelligence

Artificial Intelligence (AI) systems encode not just statistical models and complex algorithms designed to process and analyze data, but also significant normative baggage. This ethical dimension, derived from the underlying code and training data, shapes the recommendations given, behaviors exhibited, and perceptions had by AI. These factors influence how AI is regulated, used, misused, and impacts end-users. The multifaceted nature of AI’s influence has sparked extensive discussions across disciplines like Science and Technology Studies (STS), Ethical, Legal and Social Implications (ELSI) studies, public policy analysis, and responsible innovation—underscoring the need to examine AI’s ethical ramifications. While the initial wave of AI ethics focused on articulating principles and guidelines, recent scholarship increasingly emphasizes the practical implementation of ethical principles, regulatory oversight, and mitigating unforeseen negative consequences. Drawing from the concept of “ethics dumping” in research ethics, this paper argues that practices surrounding AI development and deployment can, unduly and in a very concerning way, offload ethical responsibilities from developers and regulators to ill-equipped users and host environments. Four key trends illustrating such ethics dumping are identified: (1) AI developers embedding ethics through coded value assumptions, (2) AI ethics guidelines promoting broad or unactionable principles disconnected from local contexts, (3) institutions implementing AI systems without evaluating ethical implications, and (4) decision-makers enacting ethical governance frameworks disconnected from practice. Mitigating AI ethics dumping requires empowering users, fostering stakeholder engagement in norm-setting, harmonizing ethical guidelines while allowing flexibility for local variation, and establishing clear accountability mechanisms across the AI ecosystem.

IMPACT OF QUALITY CERTIFICATION ON THE VISIBILITY AND COMPETITIVENESS OF COFFEE PRODUCTION IN CERRADO MINEIRO

The demand for sustainable agricultural practices and products that meet ethical criteria has grown among consumers, making certifications such as Fair Trade, Rainforest Alliance and UTZ essential for producers seeking to stand out in the market. This study is justified by the need to understand how certifications can add value to coffee and influence the profitability of producers, in addition to promoting practices that guarantee the economic and social sustainability of rural communities. The general objective was to investigate the impact of certifications on the visibility and competitiveness of coffee from the Cerrado Mineiro region. The methodology adopted was qualitative, with documentary analysis and literature review, following ethical guidelines and without direct interaction with producers. Data were collected from reports, academic articles and statistics from relevant institutions. The results indicated that certified coffees tend to achieve prices up to 30% higher than non-certified coffees, reflecting the willingness of consumers to invest in products that meet quality and sustainability standards. In addition, certifications encouraged the adoption of more responsible agricultural practices, generating social and environmental benefits for producing communities. The study concludes that quality certifications are essential for the competitiveness of coffee farming in Cerrado Mineiro, recommending continued support for these practices to promote a sustainable and ethical production model.

AI and Ethics: Moral Considerations of Automated Rumor Detection Technology

Abstract: As digital society continues to develop, social media and information distribution have experienced significant changes, leading to challenges with accessing credible information and identifying misinformation. In response, the use of AI automation technology has been advocated as a solution to identifying fake news and improving information credibility. However, implementing this technology raises ethical concerns related to data privacy protection, transparency in information monitoring, and maintaining a balance between free speech and censorship. This paper examines ethics associated with using AI automation techniques for identifying misinformation, and proposing relevant laws and ethical guidelines to address these concerns. Through a literature review and case analysis, this study provides guidance for the ethical implementation of AI automation techniques for identifying misinformation. Our research aims to contribute to the development of sound ethical practices in the use of AI automation techniques in misinformation identification. Furthermore, this study highlights the need for an integrated approach in AI automation technology implementation and the significant role of transparency and accountability in mitigating ethical concerns. Additionally, this paper explores the potential benefits and pitfalls associated with using AI automation techniques to identify misinformation. While such technology can improve information credibility and protect the democratic process, it also poses risks to the quality of information and public trust in technology. In conclusion, this study underscores the importance of ethical considerations in implementing AI automation techniques for identifying misinformation and provides guidance for policymakers and practitioners working in the field of AI and information governance.

ChatGPT Integration in Business Schools among Selected Higher Education Institutions: A Proposed Strategic Programs

This study investigated faculty perspectives on integrating ChatGPT into business school curricula at five universities in Manila, addressing a gap in understanding faculty acceptance, influencing factors, and challenges. Specifically, the research examined acceptance levels, key influences (perceived usefulness, social influence, innovation characteristics, and psychological needs), and barriers to implementation. A descriptive quantitative research design was employed, utilizing structured surveys with statistical analyses including correlation, regression, independent t-tests, and One-Way ANOVA. Findings revealed a generally positive faculty acceptance of ChatGPT integration, with significant correlations between perceived usefulness and social influence (r = 0.68, p < 0.01) and between innovation characteristics and psychological needs (r = 0.73, p < 0.01). Faculty acceptance also differed significantly based on experience levels (p < 0.05). Although integration is well-received, addressing critical challenges is essential for effective implementation. Recommendations include comprehensive training, robust data protection policies, ethical guidelines to prevent misuse, and a supportive environment to foster personalized learning and psychological support.

Can ChatGPT help patients understand radiopharmaceutical extravasations?

A previously published paper in the official journal of the Society of Nuclear Medicine and Molecular Imaging (SNMMI) concluded that the artificial intelligence chatbot ChatGPT may offer an adequate substitute for nuclear medicine staff informational counseling to patients in an investigated setting of 18F-FDG PET/CT. To ensure consistency with the previous paper, the author and a team of experts followed a similar methodology and evaluated whether ChatGPT could adequately offer a substitute for nuclear medicine staff informational counseling to patients regarding radiopharmaceutical extravasations. We asked ChatGPT fifteen questions regarding radiopharmaceutical extravasations. Each question or prompt was queried three times. Using the same evaluation criteria as the previously published paper, the ChatGPT responses were evaluated by two nuclear medicine trained physicians and one nuclear medicine physicist for appropriateness and helpfulness. These evaluators found ChatGPT responses to be either highly appropriate or quite appropriate in 100% of questions and very helpful or quite helpful in 93% of questions. The interobserver agreement among the evaluators, assessed using the Intraclass Correlation Coefficient (ICC), was found to be 0.72, indicating good overall agreement. The evaluators also rated the inconsistency across the three ChatGPT responses for each question and found irrelevant or minor inconsistencies in 87% of questions and some differences relevant to main content in the other 13% of the questions. One physician evaluated the quality of the references listed by ChatGPT as the source material it used in generating its responses. The reference check revealed no AI hallucinations. The evaluator concluded that ChatGPT used fully validated references (appropriate, identifiable, and accessible) to generate responses for eleven of the fifteen questions and used generally available medical and ethical guidelines to generate responses for four questions. Based on these results we concluded that ChatGPT may be a reliable resource for patients interested in radiopharmaceutical extravasations. However, these validated and verified ChatGPT responses differed significantly from official positions and public comments regarding radiopharmaceutical extravasations made by the SNMMI and nuclear medicine staff. Since patients are increasingly relying on the internet for information about their medical procedures, the differences need to be addressed.

Reflections on the "Ethics Guideline for using Generative Artificial Intelligence in Scientific Research and Publication Process of Higher Education Institutions".

Reflections on the "Ethics Guideline for using Generative Artificial Intelligence in Scientific Research and Publication Process of Higher Education Institutions".

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.

Effective Human Oversight of AI-Based Systems: A Signal Detection Perspective on the Detection of Inaccurate and Unfair Outputs

AbstractLegislation and ethical guidelines around the globe call for effective human oversight of AI-based systems in high-risk contexts – that is oversight that reliably reduces the risks otherwise associated with the use of AI-based systems. Such risks may relate to the imperfect accuracy of systems (e.g., inaccurate classifications) or to ethical concerns (e.g., unfairness of outputs). Given the significant role that human oversight is expected to play in the operation of AI-based systems, it is crucial to better understand the conditions for effective human oversight. We argue that the reliable detection of errors (as an umbrella term for inaccuracies and unfairness) is crucial for effective human oversight. We then propose that Signal Detection Theory (SDT) offers a promising framework for better understanding what affects people’s sensitivity (i.e., how well they are able to detect errors) and response bias (i.e., the tendency to report errors given a perceived evidence of an error) in detecting errors. Whereas an SDT perspective on the detection of inaccuracies is straightforward, we demonstrate its broader applicability by detailing the specifics for an SDT perspective on unfairness detection, including the need to choose a standard for (un)fairness. Additionally, we illustrate that an SDT perspective helps to better understand the conditions for effective error detection by showing examples of task-, system-, and person-related factors that may affect the sensitivity and response bias of humans tasked with detecting unfairness associated with the use of AI-based systems. Finally, we discuss future research directions for an SDT perspective on error detection.