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Nurses face several challenges in providing care for patients with coronavirus disease in 2019 (COVID-19). The study aimed to explain the nurses' perception of ethical challenges in this regard. The qualitative study was carried out using a content analysis method. Individual and semi-structured interviews were conducted with 24 nurses. Inductive content analysis was used to categorize the data. Nurses' narratives indicated that ethical challenges in caring for patients with COVID-19 included threats to professional values ​​and the absence of a holistic COVID-19 care approach. The first category was subcategorized into the risk of declining quality of patient care and a stigmatized public image about COVID-19 care. The second category was divided into poor spiritual care, poor compassionate care, and lack of family-centered care. Health care managers must develop protocols for nurses that address these issues to alleviate the ethical challenges of COVID-19 care.

This paper’s overall objective is to identify the opportunities, challenges, and effects of artificial intelligence in advertising agencies. The theoretical approach of the key concept of artificial intelligence - its definition, typologies and effects, is followed by a particularization of the term in the advertising sector, based on a synthetic review, through a secondary analysis of statistical data, of the main trends in the AI use in current Romanian and international advertising. Subsequently, the paper focuses on the applied approach, based on qualitative research carried out through interviews with both employees from advertising agencies without management positions and managers of departments and advertising agencies where the former work, in order to identify the advantages and disadvantages of working with AI, but also positive and negative effects of using AI in advertising organizations, proposals for improving activities in which AI is used and, not least, ethical challenges related to working with AI. The integration of artificial intelligence in the advertising industry presents remarkable potential for innovation and efficiency, but also the need for a responsible approach to emerging challenges. The research provided a solid foundation for understanding the complexity and dynamism of the relationship between AI and advertising, highlighting that long-term success depends on the industry's ability to navigate this ever-evolving technology landscape with caution. By fostering a harmonious collaboration between technology and creativity, the advertising sector can maximize the benefits of AI while ethically addressing the associated challenges.

Background The contemporary frameworks for clinical research require informed consent for research participation that includes disclosure of material information, comprehension of disclosed information and voluntary consent to research participation. There is thus an urgent need to test, and an ethical imperative, to test, modify or refine medications or healthcare plans that could reduce patient morbidity, lower healthcare costs or strengthen healthcare systems. Methods Conceptual review. Discussion Although some allocation principles seem better than others, no single moral principle allocates interventions justly, necessitating combining the moral principles into multiprinciple allocation systems. The urgency notwithstanding, navigating ethical challenges related to conducting corona virus disease (COVID-19) clinical trials is mandatory, in order to safeguard the safety and welfare of research participants, ensure autonomy of participants, reduce possibilities for exploitation and ensure opportunities for research participation. The ethical challenges to can be categorized as challenges in allocation of resources for research; challenges of clinical equipoise in relation to the research questions; challenges of understanding disclosed information in potential participants; and challenges in obtaining informed consent. Conclusion To navigate these challenges, stakeholders need a delicate balance of moral principles during allocation of resources for research. Investigators need to apply information processing theories to aid decision-making about research participation or employ acceptable modifications to improve the informed consent process. Research and ethics committees should strengthen research review and oversight to ensure rigor, responsiveness and transparency.

This paper conducts a systematic review and interdisciplinary analysis of the ethical challenges of generative AI technologies (N = 37), highlighting significant concerns such as privacy, data protection, copyright infringement, misinformation, biases, and societal inequalities. The ability of generative AI to produce convincing deepfakes and synthetic media, which threaten the foundations of truth, trust, and democratic values, exacerbates these problems. The paper combines perspectives from various disciplines, including education, media, and healthcare, underscoring the need for AI systems that promote equity and do not perpetuate social inequalities. It advocates for a proactive approach to the ethical development of AI, emphasizing the necessity of establishing policies, guidelines, and frameworks that prioritize human rights, fairness, and transparency. The paper calls for a multidisciplinary dialogue among policymakers, technologists, and researchers to ensure responsible AI development that conforms to societal values and ethical standards. It stresses the urgency of addressing these ethical concerns and advocates for the development of generative AI in a socially beneficial and ethically sound manner, contributing significantly to the discourse on managing AI’s ethical implications in the modern digital era. The study highlights the theoretical and practical implications of these challenges and suggests a number of future research directions.

In the management and business ethics literatures, stakeholder engagement has been demonstrated to lead to more ethical management practices. However, there may be limits on the extent to which stakeholder engagement can, as currently conceptualized, resolve some of the more difficult ethical challenges faced by managers. In this paper we argue that stakeholder engagement, when seen as a way of reducing five types of knowledge problems—risk, ambiguity, complexity, equivocality, and a priori irreducible uncertainty—can aid managers in resolving such ethical challenges. Using a practical illustration of the ethical challenges surrounding the development and application of genetic modification technologies, we demonstrate how stakeholder engagement enables managers to better address these knowledge problems, thereby to manage more ethically. In this way, we suggest that stakeholder engagement has an even more crucial role to play in business ethics research and practice.

This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.

Virtual assistants (VAs) have gained widespread popularity across a wide range of applications, and the integration of Large Language Models (LLMs), such as ChatGPT, has opened up new possibilities for developing even more sophisticated VAs. However, this integration poses new ethical issues and challenges that must be carefully considered, particularly as these systems are increasingly used in public services: transfer of personal data, decision-making transparency, potential biases, and privacy risks. This paper, an extension of the work presented at IberSPEECH 2022, analyzes the current regulatory framework for AI-based VAs in Europe and delves into ethical issues in depth, examining potential benefits and drawbacks of integrating LLMs with VAs. Based on the analysis, this paper argues that the development and use of VAs powered by LLMs should be guided by a set of ethical principles that prioritize transparency, fairness, and harm prevention. The paper presents specific guidelines for the ethical use and development of this technology, including recommendations for data privacy, bias mitigation, and user control. By implementing these guidelines, the potential benefits of VAs powered by LLMs can be fully realized while minimizing the risks of harm and ensuring that ethical considerations are at the forefront of the development process.

This study consolidates contemporary methodologies for applying artificial intelligence in software engineering. Using the PRISMA protocol, an analysis of 60 peer-reviewed publications was conducted. Findings indicate that the use of generative tools (such as GitHub Copilot), AI-based testing platforms (like Testim.io and Diffblue), and DevOps automation systems (e.g., Harness.io) can lead to a 20–40% reduction in development time, while also enhancing code quality and minimizing errors. A key academic contribution of the research is the introduction of a three-tier classification of integration barriers – technical, organizational, and legal – that hinder the seamless adoption of AI technologies within the Software Development Life Cycle (SDLC), as well as the lack of standardized methodologies. The recommendations provided in this work are particularly relevant to software engineers, IT project leaders, and academic researchers, as they address crucial concerns related to model interpretability, system instability, the absence of unified standards, and regulatory ambiguity. The practical relevance of the study lies in presenting actionable strategies for the responsible, scalable, and ethically grounded deployment of AI-driven tools in industrial, academic, and research settings.

Business ethics (BE) professors play a crucial role in sensitizing business students toward their future ethical responsibilities. Yet, there are few papers exploring the ethical challenges these professors themselves face while teaching BE. In this qualitative paper, we rely on the lenses of ethical sensemaking and dramaturgical performance, and draw from 29 semi-structured interview conducted with BE professors from various countries and field notes from 17 h of observation of BE classes. We identify four kinds of rationalities that professors rely on for making sense of in-class ethical challenges, eventually leading them to engage in one of four corresponding types of performances. By juxtaposing high and low scores of two underlying dimensions (degree of expressivity and degree of imposition), we offer a framework of four emerging performances. Additionally, we show that professors can shift from one performance to another during the course of their interactions. We contribute to performance literature by demonstrating the plurality of performances and explaining their emergence. We also contribute to sensemaking literature by offering support to its recent turn from an episodic (crises or disruption-based) to a relational, interactional, and present-oriented understanding. Since professors’ performances have an impact not only on their own teaching experiences but also on students’ learning experiences, undermining these would result in compromising the efforts that business schools have been making toward sensitizing future managers to their ethical responsibilities.

This paper investigates the integration of ChatGPT into educational environments, focusing on its potential to enhance personalized learning and the ethical concerns it raises. Through a systematic literature review, interest analysis, and case studies, the research scrutinizes the application of ChatGPT in diverse educational contexts, evaluating its impact on teaching and learning practices. The key findings reveal that ChatGPT can significantly enrich education by offering dynamic, personalized learning experiences and real-time feedback, thereby boosting teaching efficiency and learner engagement. However, the study also highlights significant challenges, such as biases in AI algorithms that may distort educational content and the inability of AI to replicate the emotional and interpersonal dynamics of traditional teacher–student interactions. The paper acknowledges the fast-paced evolution of AI technologies, which may render some findings obsolete, underscoring the need for ongoing research to adapt educational strategies accordingly. This study provides a balanced analysis of the opportunities and challenges of ChatGPT in education, emphasizing ethical considerations and offering strategic insights for the responsible integration of AI technologies. These insights are valuable for educators, policymakers, and researchers involved in the digital transformation of education.