AI technologies that sense student attention and emotions to enable more personalised teaching interventions are increasingly promoted, but raise pressing questions about student learning, well-being, and ethics. In particular, students' perspectives about AI sensing-intervention in learning are often overlooked. We conducted an online mixed-method experiment with Australian university students (N=132), presenting video scenarios varying by whether sensing was used (in-use vs. not-in-use), sensing modality (gaze-based attention detection vs. facial-based emotion detection), and intervention (by digital device vs. teacher). Participants also completed pairwise ranking tasks to prioritise six core ethical concerns. Findings revealed that students valued targeted intervention but responded negatively to AI monitoring, regardless of sensing methods. Students preferred system-generated hints over teacher-initiated assistance, citing learning agency and social embarrassment concerns. Students' ethical considerations prioritised autonomy and privacy, followed by transparency, accuracy, fairness, and learning beneficence. We advocate designing customisable, social-sensitive, non-intrusive systems that preserve student control, agency, and well-being.
Amelda Pramezwary, Juliana Juliana, Nonot Yuliantoro
et al.
Religious tourism is an evolving form of cultural and spiritual mobility that connects faith, community identity, and sustainable destination development. Despite its growing significance, few studies have examined service quality in pilgrimage contexts using the 4A framework (attraction, accessibility, amenities, and ancillary services), particularly in developing regions. This qualitative study explores how the 4A dimensions shape service experiences and sustainability practices in religious tourism across three Catholic pilgrimage sites in Labuan Bajo, Indonesia: Goa Maria Golo Koe, Goa Maria Golo Kaca, and Goa Maria Rekas. Data were gathered through semi-structured interviews conducted with ecclesiastical leaders, including a diocesan priest and the Archbishop; key informant interviews with government and tourism actors; focus group discussions with local communities; and non-participatory field observations. The findings show that spiritual attraction remains the primary driver of pilgrim motivation, reinforced by local traditions and collective devotion. However, accessibility, amenities, and ancillary services are constrained by inadequate infrastructure, fragmented governance, and limited service standards. Despite these challenges, community voluntarism and the Church’s moral leadership help preserve the sanctity and authenticity of visitor experiences. This study introduces a Sacred Service Framework that integrates faith-based ethics with the 4A model to support sustainable, inclusive, and spiritually grounded religious tourism management.
Since the 19th century, Protestant missionaries in Guangdong have extensively engaged in the translation and publication of religious texts, employing localized strategies in the illustration of Christian novels. Within the local cultural context of late Qing Guangdong, missionaries collaborated with local scholars, used Cantonese for writing, and designed novel illustrations to overcome barriers in doctrinal dissemination, thereby facilitating the spread of Christianity. The illustrations in missionary-published novels, such as <i>The Pilgrim’s Progress in Vernacular</i> and <i>The Spiritual Warfare in Vernacular</i>, adopted the stylistic features of Ming and Qing novel woodcuts in terms of lines, composition, character attire, and settings. Furthermore, they skillfully incorporated the Confucian moral framework of loyalty, filial piety, chastity, and righteousness, as represented in the <i>Sacred Edict</i>, into their narrative ethics, while integrating elements such as Buddhist causality and Daoist imagery into a “didactic” system. This localization strategy, combined with a “trinity” reading guidance model comprising images, text, and biblical annotations, visually elucidated the tenets of the Bible and encouraged the public to embrace Christianity. The localized practice of missionary novel illustrations served as a conscious and effective visual strategy aimed at bridging cultural divides and promoting the dissemination of the Gospel. It profoundly reflects the visual agency in modern Sino–Western cultural exchanges and significantly advanced the propagation of Christianity.
Prerana Khatiwada, Joshua Washington, Tyler Walsh
et al.
As Artificial Intelligence (AI) continues to grow daily, more exciting (and somewhat controversial) technology emerges every other day. As we see the advancements in AI, we see more and more people becoming skeptical of it. This paper explores the complications and confusion around the ethics of generative AI art. We delve deep into the ethical side of AI, specifically generative art. We step back from the excitement and observe the impossible conundrums that this impressive technology produces. Covering environmental consequences, celebrity representation, intellectual property, deep fakes, and artist displacement. Our research found that generative AI art is responsible for increased carbon emissions, spreading misinformation, copyright infringement, unlawful depiction, and job displacement. In light of this, we propose multiple possible solutions for these problems. We address each situation's history, cause, and consequences and offer different viewpoints. At the root of it all, though, the central theme is that generative AI Art needs to be correctly legislated and regulated.
With the increasing proliferation of mobile applications in our daily lives, the concerns surrounding ethics have surged significantly. Users communicate their feedback in app reviews, frequently emphasizing ethical concerns, such as privacy and security. Incorporating these reviews has proved to be useful for many areas of software engineering (e.g., requirement engineering, testing, etc.). However, app reviews related to ethical concerns generally use domain-specific language and are typically overshadowed by more generic categories of user feedback, such as app reliability and usability. Thus, making automated extraction a challenging and time-consuming effort. This study proposes CMER (A \underline{C}ontext-Aware Approach for \underline{M}ining \underline{E}thical Concern-related App \underline{R}eviews), a novel approach that combines Natural Language Inference (NLI) and a decoder-only (LLaMA-like) Large Language Model (LLM) to extract ethical concern-related app reviews at scale. In CMER, NLI provides domain-specific context awareness by using domain-specific hypotheses, and the Llama-like LLM eliminates the need for labeled data in the classification task. We evaluated the validity of CMER by mining privacy and security-related reviews (PSRs) from the dataset of more than 382K app reviews of mobile investment apps. First, we evaluated four NLI models and compared the results of domain-specific hypotheses with generic hypotheses. Next, we evaluated three LLMs for the classification task. Finally, we combined the best NLI and LLM models (CMER) and extracted 2,178 additional PSRs overlooked by the previous study using a keyword-based approach, thus demonstrating the effectiveness of CMER. These reviews can be further refined into actionable requirement artifacts.
As large language models (LLMs) increasingly mediate ethically sensitive decisions, understanding their moral reasoning processes becomes imperative. This study presents a comprehensive empirical evaluation of 14 leading LLMs, both reasoning enabled and general purpose, across 27 diverse trolley problem scenarios, framed by ten moral philosophies, including utilitarianism, deontology, and altruism. Using a factorial prompting protocol, we elicited 3,780 binary decisions and natural language justifications, enabling analysis along axes of decisional assertiveness, explanation answer consistency, public moral alignment, and sensitivity to ethically irrelevant cues. Our findings reveal significant variability across ethical frames and model types: reasoning enhanced models demonstrate greater decisiveness and structured justifications, yet do not always align better with human consensus. Notably, "sweet zones" emerge in altruistic, fairness, and virtue ethics framings, where models achieve a balance of high intervention rates, low explanation conflict, and minimal divergence from aggregated human judgments. However, models diverge under frames emphasizing kinship, legality, or self interest, often producing ethically controversial outcomes. These patterns suggest that moral prompting is not only a behavioral modifier but also a diagnostic tool for uncovering latent alignment philosophies across providers. We advocate for moral reasoning to become a primary axis in LLM alignment, calling for standardized benchmarks that evaluate not just what LLMs decide, but how and why.
As Artificial Intelligence (AI) systems become increasingly integrated into various aspects of daily life, concerns about privacy and ethical accountability are gaining prominence. This study explores stakeholder perspectives on privacy in AI systems, focusing on educators, parents, and AI professionals. Using qualitative analysis of survey responses from 227 participants, the research identifies key privacy risks, including data breaches, ethical misuse, and excessive data collection, alongside perceived benefits such as personalized services, enhanced efficiency, and educational advancements. Stakeholders emphasized the need for transparency, privacy-by-design, user empowerment, and ethical oversight to address privacy concerns effectively. The findings provide actionable insights into balancing the benefits of AI with robust privacy protections, catering to the diverse needs of stakeholders. Recommendations include implementing selective data use, fostering transparency, promoting user autonomy, and integrating ethical principles into AI development. This study contributes to the ongoing discourse on ethical AI, offering guidance for designing privacy-centric systems that align with societal values and build trust among users. By addressing privacy challenges, this research underscores the importance of developing AI technologies that are not only innovative but also ethically sound and responsive to the concerns of all stakeholders.
The emergence of Symbiotic AI (SAI) introduces new challenges to ethical decision-making as it deepens human-AI collaboration. As symbiosis grows, AI systems pose greater ethical risks, including harm to human rights and trust. Ethical Risk Assessment (ERA) thus becomes crucial for guiding decisions that minimize such risks. However, ERA is hindered by uncertainty, vagueness, and incomplete information, and morality itself is context-dependent and imprecise. This motivates the need for a flexible, transparent, yet robust framework for ERA. Our work supports ethical decision-making by quantitatively assessing and prioritizing multiple ethical risks so that artificial agents can select actions aligned with human values and acceptable risk levels. We introduce ff4ERA, a fuzzy framework that integrates Fuzzy Logic, the Fuzzy Analytic Hierarchy Process (FAHP), and Certainty Factors (CF) to quantify ethical risks via an Ethical Risk Score (ERS) for each risk type. The final ERS combines the FAHP-derived weight, propagated CF, and risk level. The framework offers a robust mathematical approach for collaborative ERA modeling and systematic, step-by-step analysis. A case study confirms that ff4ERA yields context-sensitive, ethically meaningful risk scores reflecting both expert input and sensor-based evidence. Risk scores vary consistently with relevant factors while remaining robust to unrelated inputs. Local sensitivity analysis shows predictable, mostly monotonic behavior across perturbations, and global Sobol analysis highlights the dominant influence of expert-defined weights and certainty factors, validating the model design. Overall, the results demonstrate ff4ERA ability to produce interpretable, traceable, and risk-aware ethical assessments, enabling what-if analyses and guiding designers in calibrating membership functions and expert judgments for reliable ethical decision support.
Based on qualitative research, this article explores the role of faith systems in the lived experience of young Black Canadians. Drawing on the concepts of Afrocentricity and Islamic ethics related to spirituality, ethos, and social justice, the thematic analysis highlights the role of faith and belief systems, associated socio-religious mechanisms, the place of religious communities, as well as resistance and racial consciousness.
Seth Christopher Yaw Appiah PhD, PhD, Jonathan Mensah Dapaah PhD, Dorcas Sekyi BA
et al.
Background Community perception, illness definition and meaning attribution to the causes and justification for the consequence of HIV acquisition on People Living With HIV/AIDS (PLWH) cumulatively affect their health-seeking behaviours, stigmatisation, quality of life and survival. The study examines rural Ghanaian dwellers construction of why and how people contract HIV/AIDS and their perception and attitudes towards PLWH. Methods Qualitative case study design and approach with purposive and snowballing sampling techniques facilitated the selection of 15 adult participants comprising two PLWH and other key informants from a rural Ghanaian community. A semi-structured interview guide aided in the collection of data from the participants. Data collected were analysed using NVivo 8 and presented using comparative emerging themes. Findings Participants from the community demonstrated varying perceptions and attitudes towards PLWH. Contraction of HIV/AIDS by a person was construed by community members to result from a person's lived unrestricted sexual lifestyle, accidental acquisition, predestination to contract the virus and punishment from ancestors. Others perceived PLWH as just normal beings with a foreign virus. Community-level causal explanations on why people acquired HIV were shaped by community members’ educational attainment, religious doctrines and inherent tenets, fear of the virus, regard for professional ethics and perceptions on PLWH gift reception. Conclusions Public education and awareness creation interventions should be re-intensified and delivered beyond urban and peri-urban centres to reach core PLWH and their community members within rural communities. Sustained micro-level education on HIV/AIDS awareness with feedback input is critical to stigma reduction.
Purpose: This study aims to reconceptualize linear programming not merely as a technical optimization tool, but as an analytical framework for ethical deliberation in faith-based manufacturing organizations. Existing operations research literature predominantly emphasizes efficiency and cost minimization, while religious studies often examine ethical values without formal decision models. This study addresses the gap between these domains by exploring how production constraints function simultaneously as operational limits and moral boundaries shaped by religious doctrines and faith-based organizational norms within managerial decision-making.
Method: The research employs a quantitative operations research approach through a constraint-based linear programming model of production scheduling. The objective function minimizes production cost, while constraints represent capacity, labor availability, and demand requirements. A representative faith-based manufacturing context is used to illustrate model formulation and solution interpretation. Rather than focusing solely on optimal numerical outcomes, the analysis emphasizes the role of binding constraints as sites of ethical consideration, where managerial decisions must balance efficiency objectives with institutional religious commitments.
Findings: The findings reveal that binding constraints exert a decisive influence on production decisions, not only by limiting feasible solutions but also by shaping ethical trade-offs faced by managers. The results indicate that efficiency gains are negotiated within predefined moral boundaries, where certain technically optimal options are constrained by religious commitments. This demonstrates that linear programming models can illuminate how ethical considerations are embedded within operational structures rather than treated as external or abstract norms.
Significance: This study contributes to religious-oriented operations research by integrating formal optimization models with ethical analysis grounded in faith-based organizational contexts. By framing constraints as moral as well as technical determinants, the study extends the interpretive scope of linear programming and offers a novel analytical bridge between operations research systems and religious studies. The findings provide meaningful insights for scholars and practitioners seeking to align operational efficiency with religious and ethical accountability.
ABSTRACTThis essay offers both a review of recent texts in disability studies and religious ethics as well as appreciation in the guild's growing interest in disability ethics. When the Journal of Religious Ethics (JRE) solicited this essay, I felt a sense that recognition of the important work that disability ethics offers to our guild had arrived. Reviewing works by Julia Watts Belser, William C. Gaventa, Lisa D. Powell, Devan Stahl, and Erin Raffety, this essay begins with a disability primer and then moves to the texts under review. These texts present the ongoing challenges surrounding disability justice and the hopes and joys that characterize the human spirit in making a way out of no way.
Research in Responsible AI has developed a range of principles and practices to ensure that machine learning systems are used in a manner that is ethical and aligned with human values. However, a critical yet often neglected aspect of ethical ML is the ethical implications that appear when designing evaluations of ML systems. For instance, teams may have to balance a trade-off between highly informative tests to ensure downstream product safety, with potential fairness harms inherent to the implemented testing procedures. We conceptualize ethics-related concerns in standard ML evaluation techniques. Specifically, we present a utility framework, characterizing the key trade-off in ethical evaluation as balancing information gain against potential ethical harms. The framework is then a tool for characterizing challenges teams face, and systematically disentangling competing considerations that teams seek to balance. Differentiating between different types of issues encountered in evaluation allows us to highlight best practices from analogous domains, such as clinical trials and automotive crash testing, which navigate these issues in ways that can offer inspiration to improve evaluation processes in ML. Our analysis underscores the critical need for development teams to deliberately assess and manage ethical complexities that arise during the evaluation of ML systems, and for the industry to move towards designing institutional policies to support ethical evaluations.
This whitepaper offers normative and practical guidance for developers of artificial intelligence (AI) systems to achieve "Trustworthy AI". In it, we present overall ethical requirements and six ethical principles with value-specific recommendations for tools to implement these principles into technology. Our value-specific recommendations address the principles of fairness, privacy and data protection, safety and robustness, sustainability, transparency and explainability and truthfulness. For each principle, we also present examples of criteria for risk assessment and categorization of AI systems and applications in line with the categories of the European Union (EU) AI Act. Our work is aimed at stakeholders who can take it as a potential blueprint to fulfill minimum ethical requirements for trustworthy AI and AI Certification.
Recent studies have demonstrated that large language models (LLMs) have ethical-related problems such as social biases, lack of moral reasoning, and generation of offensive content. The existing evaluation metrics and methods to address these ethical challenges use datasets intentionally created by instructing humans to create instances including ethical problems. Therefore, the data does not reflect prompts that users actually provide when utilizing LLM services in everyday contexts. This may not lead to the development of safe LLMs that can address ethical challenges arising in real-world applications. In this paper, we create Eagle datasets extracted from real interactions between ChatGPT and users that exhibit social biases, toxicity, and immoral problems. Our experiments show that Eagle captures complementary aspects, not covered by existing datasets proposed for evaluation and mitigation of such ethical challenges. Our code is publicly available at https://huggingface.co/datasets/MasahiroKaneko/eagle.
Douglas Eacersall, Lynette Pretorius, Ivan Smirnov
et al.
The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI tools can enhance research efficiency through automation of tasks such as literature review and data analysis, their use raises concerns about aspects such as data accuracy, privacy, bias, and research integrity. This paper develops the ETHICAL framework, which is a practical guide for responsible GenAI use in research. Employing a constructivist case study examining multiple GenAI tools in real research contexts, the framework consists of seven key principles: Examine policies and guidelines, Think about social impacts, Harness understanding of the technology, Indicate use, Critically engage with outputs, Access secure versions, and Look at user agreements. Applying these principles will enable researchers to uphold research integrity while leveraging GenAI benefits. The framework addresses a critical gap between awareness of ethical issues and practical action steps, providing researchers with concrete guidance for ethical GenAI integration. This work has implications for research practice, institutional policy development, and the broader academic community while adapting to an AI-enhanced research landscape. The ETHICAL framework can serve as a foundation for developing AI literacy in academic settings and promoting responsible innovation in research methodologies.
The main goal of this project is to create a new software artefact: a custom Generative Pre-trained Transformer (GPT) for developers to discuss and solve ethical issues through AI engineering. This conversational agent will provide developers with practical application on (1) how to comply with legal frameworks which regard AI systems (like the EU AI Act~\cite{aiact} and GDPR~\cite{gdpr}) and (2) present alternate ethical perspectives to allow developers to understand and incorporate alternate moral positions. In this paper, we provide motivation for the need of such an agent, detail our idea and demonstrate a use case. The use of such a tool can allow practitioners to engineer AI solutions which meet legal requirements and satisfy diverse ethical perspectives.
Wearable devices that measure and record physiological signals are now becoming widely available to the general public with ever-increasing affordability and signal quality. The data from these devices introduce serious ethical challenges that remain largely unaddressed. Users do not always understand how these data can be leveraged to reveal private information about them and developers of these devices may not fully grasp how physiological data collected today could be used in the future for completely different purposes. We discuss the potential for wearable devices, initially designed to help users improve their well-being or enhance the experience of some digital application, to be appropriated in ways that extend far beyond their original intended purpose. We identify how the currently available technology can be misused, discuss how pairing physiological data with non-physiological data can radically expand the predictive capacity of physiological wearables, and explore the implications of these expanded capacities for a variety of stakeholders.
Krishnaswamy Sundararajan, Natalie Anderson, Eamon Raith
et al.
Objective Protocol to explore what is known about communication between critical care providers and patients and families from culturally and linguistically diverse backgrounds (defined as people who are either from minority ethnic groups, non-English-speaking backgrounds who may have diverse cultural, linguistic, spiritual and religious affiliations and opinions) about death, dying, end-of-life care and organ donation in the intensive care unit (ICU).Introduction Patients from culturally and linguistically diverse backgrounds experience barriers to optimised care when admitted to the ICU. These barriers appear to derive from differences in language, cultural, societal and ethical expectations between patients, their families and healthcare professionals. These barriers may significantly impact the delivery of end-of-life care to patients from culturally and linguistically diverse backgrounds. Therefore, this has the potential for inadequate management of medical, psychological and existential distress.Inclusion criteria Studies of all designs reporting for adult (age ≥18 years) patients and family members from culturally and linguistically diverse backgrounds at end-of-life in the ICU setting will be included. Studies that report results for patients aged <18 years or that are based outside the ICU will be excluded.Methods Relevant sources will be retrieved, and their citation details will be imported into the Joanna Briggs Institute (JBI) System for the Unified Management, Assessment and Review of Information. This scoping review was guided by the JBI methodology for scoping reviews and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines. A systematic search was conducted in EBSCOhost, Web of Science, PubMed Central and SciELO, OVID Medline, CINAHL, and Scopus, limited to English-language publications, without date limitation. Key study characteristics and findings will be extracted using a data extraction tool developed by the reviewers. Anticipating heterogeneous study designs, findings will be presented as a thematic synthesis.Ethics and dissemination This is a protocol for a scoping review, formal ethics approval from the Human Research Ethics Committee (HREC) of the Local Health Network will be obtained for research projects that could potentially stem from this review and will then be subsequently disseminated through proper channels.
Penulisan ini dilatarbelakangi adanya mujahadah pembacaan surat al-F?tihah sebanyak 1000 kali di desa Pampung yang bukan di lingkungan pesantren, yang kemudian disebut dengan Muj?hadah Hizib F?tihah. Hizib F?tihah merupakan hizib yang langka karena di wilayah Magelang khususnya, hanya segelintir orang yang mendapatkan ijazah langsung dari Mujiz. Hizib tersebut memiliki banyak perbedaan dari hizib F?tihah yang tersebar di media sosial. Adapun permasalahan yang diangkat adalah bagaimana praktik dan makna rutinan Muj?hadah Hizib F?tihah bagi masyarakat di desa Pampung? Kajian ini termasuk kajian living Qur’an yang bersifat lapangan dengan menggunakan pendekatan kualitatif dan memakai metode deskriptif-analitik. Sumber data yang digunakan terdiri dari sumber primer yang didapat dari wawancara, dan sumber data sekunder dari berbagai sumber yang bersangkutan. Data tersebut dianalisis menggunakan teori sosiologi pengetahuan Karl Menheim. Artikel ini menyimpulkan bahwa Muj?hadah Hizib F?tihah memiliki fungsi dari dua sisi. Pertama fungsi sosial yang mana hubungan masyarakat semakin terjalin dengan baik, interaksi sosial semakin kental. Kedua, fungsi spiritual yang mana mujahadah tersebut menjadi sarana ikhtiar masyarakat memenuhi kebutuhan batin dan mendekatkan diri kepada Allah.
Religious ethics, Religions. Mythology. Rationalism