This study aims to determine the moderating value contained in Geguritan Dharma Sunyata. The data in this study were collected using document study techniques. Furthermore, it is analyzed with the stages of data reduction, data presentation and drawing conclusions. From the results of data analysis, it was found that the value of religious moderation contained in Geguritan Dharma Sunyata was based on the teachings of Tat Twam Asi and Tri Kaya Parisudha. Tat Twam Asi's teachings are the basis of Hindu ethics in an effort to achieve moral improvement. Tat Twam Asi is a teaching that states the similarities between individuals so that it gives birth to the concept of compassion for all creatures in the world. Religious people should respect and appreciate each other even though they adhere to different religions.
Octavia Wulandari, Arif Surya Volta, Marzuki Marzuki
et al.
This study aims to analyze the integration and implementation of Digital Citizenship Education (DCE) in Indonesia's national curriculum as a preventive measure against the increasing cases of cyberbullying among teenagers. The study was conducted through a literature review of curriculum policy documents and relevant journal articles. The results show that cases of cyberbullying in Indonesia continue to increase, especially among young people who use social media. Although the government has strengthened regulations through revisions to the Electronic Information and Transactions Law, law enforcement has not been effective enough without the support of digital values and ethics education in schools. An analysis of the Merdeka Curriculum reveals that PKD is not yet a separate subject, but is integrated into certain subjects such as Informatics, Religious Education and Morality, and Pancasila Education, and is implemented across the curriculum. This integration is still implicit and focuses on digital literacy. Therefore, it is necessary to develop a systematic and applicable PKD learning model, emphasizing four main elements in preventing cyberbullying, namely digital communication, digital literacy, digital etiquette, and digital law. These four elements are important in shaping the character of digital citizens who are smart, ethical, and responsible.
Keywords: Digital Citizenship Education, Cyberbullying, Indonesia
Special aspects of education, Social sciences and state - Asia (Asian studies only)
Ethics review is a foundational mechanism of modern research governance, yet contemporary systems face increasing strain as ethical risks arise as structural consequences of large-scale, interdisciplinary scientific practice. The demand for consistent and defensible decisions under heterogeneous risk profiles exposes limitations in institutional review capacity rather than in the legitimacy of ethics oversight. Recent advances in large language models (LLMs) offer new opportunities to support ethics review, but their direct application remains limited by insufficient ethical reasoning capability, weak integration with regulatory structures, and strict privacy constraints on authentic review materials. In this work, we introduce Mirror, an agentic framework for AI-assisted ethical review that integrates ethical reasoning, structured rule interpretation, and multi-agent deliberation within a unified architecture. At its core is EthicsLLM, a foundational model fine-tuned on EthicsQA, a specialized dataset of 41K question-chain-of-thought-answer triples distilled from authoritative ethics and regulatory corpora. EthicsLLM provides detailed normative and regulatory understanding, enabling Mirror to operate in two complementary modes. Mirror-ER (expedited Review) automates expedited review through an executable rule base that supports efficient and transparent compliance checks for minimal-risk studies. Mirror-CR (Committee Review) simulates full-board deliberation through coordinated interactions among expert agents, an ethics secretary agent, and a principal investigator agent, producing structured, committee-level assessments across ten ethical dimensions. Empirical evaluations demonstrate that Mirror significantly improves the quality, consistency, and professionalism of ethics assessments compared with strong generalist LLMs.
ABSTRACTDoes religious ethics have anything meaningful to say about the many difficult metaphysical, ethical, and theological questions surrounding artificial intelligence (AI)? The four articles featured in this Focus Issue suggest that it does. Mariele Courtois's essay focuses on the cultivation of prudence as a necessary virtue for the moral life and raises concerns about how increasing reliance on AI may distort the nature of practical moral reasoning. Paul Scherz and Luis Vera's coauthored essay draws attention to an emerging crisis of the knowing self in the post‐truth age of AI, arguing that the self is becoming alienated from the production of knowledge. Kevin Jung's essay explores how language can serve as a window into both the artificial and human mind, drawing insights from Augustine and Wittgenstein. John Pittard's essay examines the moral standing of AGIs and weighs the reasons for and against their development from a Christian perspective.
This article compares the outcomes of studies using the so-called Humans and Nature scale in The Netherlands and in Indonesia. The scale measures public support for policies that aim to address environmental challenges, conceptualizing and operationalizing 4 images of human-nature interaction, based on philosophy and religion. The scale was developed in The Netherlands, which is considered to be one of the most secularized countries in the world. It has been used in more than 12 countries, and recently in Indonesia, a country that is considered to be overwhelmingly religious, predominantly Muslim. The main research questions are whether religion matters when it comes to environment, and whether The Netherlands and Indonesia differ in this respect. The answers are relevant because in the environmentalism debate there is a tension between secular and religious environmentalists that does not facilitate a joint effort. The main finding is that humans primarily respond to environmental issues as humans, not as Muslims or Christians, Indonesians or Dutch, and that respondents of various backgrounds are united in their support for a view of humans as eco-friendly stewards of nature. However, this fundamental human attitude towards nature may be framed in religious language, if that language is available in a specific context. In a world where there seems to be a growing gap between the West and the Muslim world, yet a world which faces global environmental challenges, this outcome might be surprising and significant. It is good news for policy makers who foster collaboration between religions, religious and secular (non-religious) actors, and Westerners and non-Westerners in overcoming environmental challenges.
Aris Joko Riyanto, Muhammad Hanif, Nurhadji Nugraha
ABSTRACT
This research aims to determine whether the use of Artificial Intelligence (AI) can improve students' social studies learning outcomes. The social studies learning outcomes of class 8D students increased after implementing AI-based learning over two cycles, with the following results: (1) In the pre-cycle, students' social studies learning outcomes were in the poor category with an average score of 68.79. Only 11 students (37.93%) achieved the Minimum Mastery Criteria (KKTP). Classically, students did not achieve KKTP in social studies learning; (2) In the first cycle, students' learning outcomes were in the sufficient category with an average score of 74.14. As many as 20 students (68.97%) achieved KKTP. Classically, students did not achieve KKTP in social studies learning; (3) In the second cycle, students' learning outcomes were in the good category, with an average score of 83.10. There were 25 students (86.21%) who successfully achieved KKTP. Classically, students achieved KKTP in social studies learning. Based on the results of data analysis, it can be concluded that the use of AI in social studies learning can improve the social studies learning outcomes of class 8D students at SMP Negeri 12 Madiun.
ABSTRAK
Penelitian ini bertujuan untuk mengetahui apakah penggunaan Artificial Intelligence (AI) dapat meningkatkan hasil belajar IPS siswa. Hasil belajar IPS siswa kelas 8 D meningkat setelah dilaksanakan pembelajaran dengan menggunakan AI selama 2 siklus dengan hasil sebagai berikut: (1) Hasil belajar IPS siswa pada prasiklus, berada pada kategori kurang dengan nilai rata-rata adalah 68,79. Siswa yang mampu mencapai KKTP sebanyak 11 siswa (37,93%). Secara klasikal siswa tidak mencapai KKTP pada pembelajaran IPS; (2) Hasil belajar IPS siswa pada siklus I berada pada kategori cukup dengan nilai rata-rata adalah 74,14. Siswa yang mencapai KKTP sebanyak 20 siswa (68,97%). Secara klasikal siswa tidak mencapai KKTP pada pembelajaran IPS; (3) Hasil belajar siswa pada siklus II, berada pada kategori baik, dengan nilai rata-rata 83,10. Terdapat 25 siswa (86,21%) yang berhasil mencapai KKTP. Secara klasikal, siswa mencapai KKTP pada pembelajaran IPS. Berdasarkan hasil analisis data dapat disimpulkan bahwa penggunaan AI dalam pembelajaran IPS dapat meningkatkan hasil belajar IPS siswa kelas 8 D SMP Negeri 12 Madiun.
Alayt Issak, Uttkarsh Narayan, Ramya Srinivasan
et al.
Ethical theories and Generative AI (GenAI) models are dynamic concepts subject to continuous evolution. This paper investigates the visualization of ethics through a subset of GenAI models. We expand on the emerging field of Visual Ethics, using art as a form of critical inquiry and the metaphor of a kaleidoscope to invoke moral imagination. Through formative interviews with 10 ethics experts, we first establish a foundation of ethical theories. Our analysis reveals five families of ethical theories, which we then transform into images using the text-to-image (T2I) GenAI model. The resulting imagery, curated as Kaleidoscope Gallery and evaluated by the same experts, revealed eight themes that highlight how morality, society, and learned associations are central to ethical theories. We discuss implications for critically examining T2I models and present cautions and considerations. This work contributes to examining ethical theories as foundational knowledge that interrogates GenAI models as socio-technical systems.
Ensuring ethical behavior in Artificial Intelligence (AI) systems amidst their increasing ubiquity and influence is a major concern the world over. The use of formal methods in AI ethics is a possible crucial approach for specifying and verifying the ethical behavior of AI systems. This paper proposes a formalization based on deontic logic to define and evaluate the ethical behavior of AI systems, focusing on system-level specifications, contributing to this important goal. It introduces axioms and theorems to capture ethical requirements related to fairness and explainability. The formalization incorporates temporal operators to reason about the ethical behavior of AI systems over time. The authors evaluate the effectiveness of this formalization by assessing the ethics of the real-world COMPAS and loan prediction AI systems. Various ethical properties of the COMPAS and loan prediction systems are encoded using deontic logical formulas, allowing the use of an automated theorem prover to verify whether these systems satisfy the defined properties. The formal verification reveals that both systems fail to fulfill certain key ethical properties related to fairness and non-discrimination, demonstrating the effectiveness of the proposed formalization in identifying potential ethical issues in real-world AI applications.
The shift towards pluralism in global data ethics acknowledges the importance of including perspectives from the Global Majority to develop responsible data science practices that mitigate systemic harms in the current data science ecosystem. Sub-Saharan African (SSA) practitioners, in particular, are disseminating progressive data ethics principles and best practices for identifying and navigating anti-blackness and data colonialism. To center SSA voices in the global data ethics discourse, we present a framework for African data ethics informed by the thematic analysis of an interdisciplinary corpus of 50 documents. Our framework features six major principles: 1) Challenge Power Asymmetries, 2) Assert Data Self-Determination, 3) Invest in Local Data Institutions & Infrastructures, 4) Utilize Communalist Practices, 5) Center Communities on the Margins, and 6) Uphold Common Good. We compare our framework to seven particularist data ethics frameworks to find similar conceptual coverage but diverging interpretations of shared values. Finally, we discuss how African data ethics demonstrates the operational value of data ethics frameworks. Our framework highlights Sub-Saharan Africa as a pivotal site of responsible data science by promoting the practice of communalism, self-determination, and cultural preservation.
We introduce NAEL (Non-Anthropocentric Ethical Logic), a novel ethical framework for artificial agents grounded in active inference and symbolic reasoning. Departing from conventional, human-centred approaches to AI ethics, NAEL formalizes ethical behaviour as an emergent property of intelligent systems minimizing global expected free energy in dynamic, multi-agent environments. We propose a neuro-symbolic architecture to allow agents to evaluate the ethical consequences of their actions in uncertain settings. The proposed system addresses the limitations of existing ethical models by allowing agents to develop context-sensitive, adaptive, and relational ethical behaviour without presupposing anthropomorphic moral intuitions. A case study involving ethical resource distribution illustrates NAEL's dynamic balancing of self-preservation, epistemic learning, and collective welfare.
This paper presents a novel methodology for classifying early modern religious images by using Large Language Models (LLMs) and vector databases in combination with Retrieval-Augmented Generation (RAG). The approach leverages the full-page context of book illustrations from the Holy Roman Empire, allowing the LLM to generate detailed descriptions that incorporate both visual and textual elements. These descriptions are then matched to relevant Iconclass codes through a hybrid vector search. This method achieves 87% and 92% precision at five and four levels of classification, significantly outperforming traditional image and keyword-based searches. By employing full-page descriptions and RAG, the system enhances classification accuracy, offering a powerful tool for large-scale analysis of early modern visual archives. This interdisciplinary approach demonstrates the growing potential of LLMs and RAG in advancing research within art history and digital humanities.
Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: knowledge, assessing the models' grasp of medical ethics principles, and application, focusing on their ability to apply these principles across diverse scenarios. To support this benchmark, we consulted with medical ethics researchers and developed three datasets addressing distinct ethical challenges: blatant violations of medical ethics, priority dilemmas with clear inclinations, and equilibrium dilemmas without obvious resolutions. MedEthicEval serves as a critical tool for understanding LLMs' ethical reasoning in healthcare, paving the way for their responsible and effective use in medical contexts.
ABSTRACTDevelopments along various epistemic fronts have been gradually modifying our ethical conception of what constitutes agency. For several reasons, we should acknowledge in particular that there is an irremediably ecological character to moral action, in a number of respects. After providing a broad analysis of the ecological turn in moral theory, I reflect on some of its implications for our understanding of the historicity of morals. I then comment on ways in which the field of comparative religious ethics can enrich this emergent account of ecological agency, and vice versa.
This study discusses the application of the ul? al-alb?b and wicked groups in the era of society 5.0 based on QS. al-Ra'd/13:19-25. This research is a library research using primary and secondary data with interpretation and sociology approaches. The results of this study are first, that the ul? al-alb?b group in QS. al-Ra'd/13:19-25 has several characteristics, namely taking lessons from everything, fulfilling promises, connecting something that is ordered to be connected, fearing painful reckoning, being patient, establishing prayer, giving alms, and repaying evil with good. Second, the wicked group in the verse are breaking promises, breaking something that is ordered to be continued, and doing damage. The three forms of application of the ul? al-alb?b group in QS. al-Ra'd/13:19-25 in the era of society 5.0, among others, using the internet wisely, as a means of connecting, as a means of giving alms, and as a means of spreading goodness. Meanwhile, the application of the wicked groups in this verse in the era of society 5.0 includes using the internet for immorality, breaking social relations or hospitality, and using it to do damage. The urgency of this research is to provide understanding to Muslims about the characteristics of ul? al-alb?b and wicked, so that they can be used to face the era of society 5.0.
Religious ethics, Religions. Mythology. Rationalism
Industry actors in the United States have gained extensive influence in conversations about the regulation of general-purpose artificial intelligence (AI) systems. Although industry participation is an important part of the policy process, it can also cause regulatory capture, whereby industry co-opts regulatory regimes to prioritize private over public welfare. Capture of AI policy by AI developers and deployers could hinder such regulatory goals as ensuring the safety, fairness, beneficence, transparency, or innovation of general-purpose AI systems. In this paper, we first introduce different models of regulatory capture from the social science literature. We then present results from interviews with 17 AI policy experts on what policy outcomes could compose regulatory capture in US AI policy, which AI industry actors are influencing the policy process, and whether and how AI industry actors attempt to achieve outcomes of regulatory capture. Experts were primarily concerned with capture leading to a lack of AI regulation, weak regulation, or regulation that over-emphasizes certain policy goals over others. Experts most commonly identified agenda-setting (15 of 17 interviews), advocacy (13), academic capture (10), information management (9), cultural capture through status (7), and media capture (7) as channels for industry influence. To mitigate these particular forms of industry influence, we recommend systemic changes in developing technical expertise in government and civil society, independent funding streams for the AI ecosystem, increased transparency and ethics requirements, greater civil society access to policy, and various procedural safeguards.
Previous discussions have highlighted the need for generative AI tools to become more culturally sensitive, yet often neglect the complexities of handling content about minorities, who are perceived differently across cultures and religions. Our study examined how two generative AI systems respond to homophobic statements with varying cultural and religious context information. Findings showed ChatGPT 3.5's replies exhibited cultural relativism, in contrast to Bard's, which stressed human rights and provided more support for LGBTQ+ issues. Both demonstrated significant change in responses based on contextual information provided in the prompts, suggesting that AI systems may adjust in their responses the degree and forms of support for LGBTQ+ people according to information they receive about the user's background. The study contributes to understanding the social and ethical implications of AI responses and argues that any work to make generative AI outputs more culturally diverse requires a grounding in fundamental human rights. A revised edition of this preprint is available open access at Big Data & Society at https://doi.org/10.1177/20539517251396069
Artificial intelligence (AI) ethics has emerged as a burgeoning yet pivotal area of scholarly research. This study conducts a comprehensive bibliometric analysis of the AI ethics literature over the past two decades. The analysis reveals a discernible tripartite progression, characterized by an incubation phase, followed by a subsequent phase focused on imbuing AI with human-like attributes, culminating in a third phase emphasizing the development of human-centric AI systems. After that, they present seven key AI ethics issues, encompassing the Collingridge dilemma, the AI status debate, challenges associated with AI transparency and explainability, privacy protection complications, considerations of justice and fairness, concerns about algocracy and human enfeeblement, and the issue of superintelligence. Finally, they identify two notable research gaps in AI ethics regarding the large ethics model (LEM) and AI identification and extend an invitation for further scholarly research.