Ethical Guidelines Research Articles

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.

AIOps and DevOps : Catalysts of Digital Transformation in the Age of Automated Operations

This article examines the pivotal roles of Artificial Intelligence for IT Operations (AIOps) and Development Operations (DevOps) as key drivers of digital transformation in contemporary organizations. As businesses increasingly rely on complex, distributed IT systems, the integration of AIOps and DevOps emerges as a critical strategy for maintaining operational efficiency, scalability, and innovation. Through a comprehensive analysis of current literature and industry case studies, we explore how these technologies are reshaping the landscape of IT operations, accelerating software delivery cycles, and enabling more resilient and adaptive infrastructures. The article delves into the evolving nature of release engineering, where automation and machine learning are becoming central to ensuring agile and robust IT systems. Furthermore, we critically assess the ethical implications of automated decision-making in operations, including issues of transparency, bias, and job displacement. Our findings suggest that while AIOps and DevOps offer significant benefits in terms of operational optimization and cost reduction, their successful implementation requires careful consideration of organizational culture, skill development, and ethical guidelines. This article contributes to the growing body of knowledge on digital transformation strategies and provides insights for practitioners navigating the complex intersection of artificial intelligence, automation, and IT operations.

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.

Artificial intelligence in management education: transformative potential and challenges

Purpose The purpose of this paper is to define artificial intelligence (AI) and examine its history, positive and negative impacts, ethical and social implications and implementation within management education. This paper offers various suggestions for the use of AI, as well as context surrounding the current AI landscape. Design/methodology/approach The paper uses a narrative review (Sylvester et al., 2013). Findings This paper identifies several areas of AI innovation, including AI tutoring systems, feedback systems for student papers, utilization of AI for innovative lesson plans and the use of AI to predict potential student dropout from a course or institution. In addition, there are significant concerns regarding the lack of ethical guidelines with current AI. Practical implications Practical implications include the ability to immediately use certain AI tools to enhance lesson plans as well as enhance student work using AI as a tool. Originality/value This paper was originally created as a conference presentation and presented at the society for advancement of management (SAM) International Business Conference before being reworked to be submitted to the journal. All content in this paper is original in their creation.

Generative artificial intelligence (GAI): From large language models (LLMs) to multimodal applications towards fine tuning of models, implications, investigations

This research explores the transformative integration of artificial intelligence (AI), robotics, and language models, with a particular emphasis on the PaLM-E model. The exploration aims to assess PaLM-E’s decision-making processes and adaptability across various robotic environments, demonstrating its capacity to convert textual prompts into very precise robotic actions. In addition, the research investigates Parameter-Efficient Fine-Tuning (PEFT) techniques, such as Low-Rank Adaptation (LoRA) and Quantized Low-Rank Adaptation (QLoRA), providing a historical overview of PEFT and highlighting their significance in enhancing task performance while reducing the number of trainable parameters. The broader scope of Generative AI is examined through an analysis of influential models like GPT-3, GPT-4, Copilot, Bard, LLaMA, Stable Diffusion, Midjourney, and DALL-E. These models’ abilities to process natural language prompts and generate a wide range of outputs are thoroughly investigated. The research traces the historical evolution of AI, from its roots in science fiction to its practical applications today, with a focus on the rise of Generative AI in the 21st century. Furthermore, the research delves into the various modalities of Generative AI, covering applications in text, code, images, and more, and assesses their real-world impact on robotics, planning, and business intelligence. The implications of synthetic data generation for business analytics are also explored. The research inspects within both software and hardware landscapes, comparing local deployment on consumer-grade hardware along with cloud-based services, and underscores the benefits of local model deployment in terms of privacy protection, intellectual property security, and censorship resistance. Ethical considerations are central to this research, addressing concerns related to privacy, security, societal impact, biases, and misinformation. The research proposes ethical guidelines for the responsible development and deployment of AI technologies. Ultimately, this work reveals the deep interconnections between vision, language, and robotics, pushing the boundaries of AI capabilities and providing crucial insights for future AI model development and technological innovation. These findings are intended to guide the field through the emerging challenges of the rapidly evolving Generative AI landscape.

Ethical climate and turnover intention among nurses: A scoping review.

Nursing shortages and turnover pose significant challenges for health organizations worldwide, driven by various organizational and individual factors. Ethical climate has emerged as a critical aspect influencing nurses' well-being and retention within healthcare settings, reflecting organizational practices with moral implications. Understanding the relationship between ethical climate and turnover intention among nurses is paramount for practitioners, managers, and policymakers. This review aimed to examine evidence and synthesize findings from prior studies on the association between ethical climate and turnover intention among nurses and identify key variables influencing its dynamics. A scoping review methodology was employed, encompassing studies retrieved up to February 2024 from major scientific databases, including PUBMED/MEDLINE, WEB OF SCIENCE, SCOPUS, and SCIENCE DIRECT. This study was conducted following ethical considerations and sound scientific guidelines. Sixteen articles meeting the inclusion criteria were identified. Overall, a significant association between ethical climate and turnover intention was observed, with theoretical underpinnings demonstrated. Key variables frequently linked to both ethical climate and turnover intention included organizational commitment, job satisfaction, and moral distress, while organizational commitment, organizational cynicism, and organizational trust emerged as mediators. Despite demonstrating a clear association, results underscore the necessity for replication studies, qualitative and longitudinal approaches, and standardized measurement. Enhancing the ethical climate through regular assessments, education, and interprofessional collaboration can improve retention. Future research should focus on theory building, ethical management and leadership, associated factors analysis, and intervention studies to develop comprehensive models and effective strategies for improving the ethical climate and reducing nurse turnover.

Clear universal clinical and ethical guidelines must be developed to prevent and reduce coercive interventions in various clinical mental health settings aiming at promoting evidenced-based and trauma-informed care.

Clear universal clinical and ethical guidelines must be developed to prevent and reduce coercive interventions in various clinical mental health settings aiming at promoting evidenced-based and trauma-informed care.

Article: The Crystal Ball: AI Predictive Analytics in Arbitration: Navigating Promise, Pitfalls, and Paradigm Shifts

The article discusses the increasing role of artificial intelligence (AI) in international arbitration, noting its potential to make the process more efficient and data-driven through predictive analytics and machine learning. However, AI may struggle with the complexities of arbitration, such as witness credibility and arbitrator biases, which are difficult to quantify. There are also risks like over-reliance on AI and potential impacts on due process. To responsibly integrate AI, certain strategies are suggested, such as developing ethical guidelines, enhancing AI literacy, implementing disclosure requirements, and ensuring human oversight. The need to preserve core values such as fairness, justice, and transparency is stressed. As AI becomes more prevalent, arbitration practitioners must adapt by acquiring new skills and focusing on a balanced approach that combines AI with human judgment. This balance is crucial to enhancing the arbitration process while maintaining its integrity and fairness.

Medical futility at the end of life: the first qualitative study of ethical decision-making methods among Turkish doctors

BackgroundThe swift advancement of intensive care medicine, coupled with technological possibilities, has prompted numerous ethical inquiries regarding decision-making processes concerning the withholding or withdrawal of treatment due to medical futility. This study seeks to delineate the decision-making approaches employed by intensive care physicians in Türkiye when faced with medical futility at the end of life, along with an ethical evaluation of these practices.MethodsGrounded theory, a qualitative analysis method was employed, conducting semi-structured, in-depth interviews with eleven intensive care physicians in Türkiye. The subsequent text analysis was carried out using MAXQDA software.ResultsParticipants assert that the decisions made by Turkish physicians determine whether treatment is futile, rely on medical consensus, and lack a standardized decision-making process. The decisions are influenced by legal and social pressures, resource constraints, and occasional conflicts of interest. The significance of professional hierarchy is notable, with limited consideration given to the opinions of nurses and other staff. The unstructured medical consensus processes are shaped by normative concepts such as benefit, age, justice, and conscience. Furthermore, it was observed that the conscientious opinions of physicians carry more weight than adherence to ethical principles and guidelines.ConclusionTo create optimal conditions for doctors to make ethically justifiable decisions, the dynamics within the treatment team should be improved, emphasizing the minimization of hierarchy, and ensuring the active participation of all team members in the decision-making process. Additionally, efforts should be directed toward narrowing the gap between the conscience of the individual doctor and established ethical principles. A potential solution lies in the nationwide implementation of clinical ethics committees and the establishing of clinical ethics guidelines, aiming to address, and overcome the identified challenges.