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This study aims to identify the pragmatic failures of Large Language Models (LLMs) and the biases of Anglophone-based AI moderation algorithms in detecting Indonesian hate speech expressed through sarcasm, satire, euphemism, and local cultural metaphors. It also examines the extent to which AI systems understand and interpret the pragmatic meanings within the corpus. This study employs a qualitative descriptive approach with a comparative design. Data were collected through the documentation of hate speech expressions on social media containing elements of local cultural hatred. The data were analyzed using qualitative descriptive methods with pragmatic and thematic approaches. The findings show that all corpus data contain political satire and indirect hate expressed through irony, sarcasm, absurd metaphors, and popular culture wordplay. Testing with Claude AI showed that the system was capable of identifying the data as implicit criticism and recognizing the pragmatic functions of emoticons and contextual meanings in the utterances. However, the analysis also demonstrated limitations in understanding local sociocultural contexts, particularly the metaphors “daun nangka” and “daun sawit,” which were interpreted merely as absurd humor. These findings indicate that AI detection accuracy does not necessarily reflect a deep pragmatic and cultural understanding within the Indonesian context.

The rapid development of Artificial Intelligence (AI) has transformed various sectors of human life, including church ministry and religious organizational management. This study aims to analyze Christian leadership ethics in the use of AI within modern church ministry. The research employs a qualitative descriptive method through theological literature review and analysis of recent studies concerning digital technology and pastoral ministry. The findings indicate that AI provides significant benefits for church administration, digital communication, congregational data management, and online evangelism. Nevertheless, ethical challenges also emerge, such as the weakening of pastoral relationships, data privacy concerns, algorithmic bias, and the risk of dehumanization in ministry practices. From a Christian theological perspective, ministry is fundamentally relational and incarnational, reflecting the example of Jesus Christ who ministered through personal presence, compassion, and direct interaction with people. Therefore, AI should be understood as a supportive instrument rather than a substitute for spiritual authority and pastoral presence. Christian leadership in the digital age must be grounded in integrity, transparency, spiritual discernment, and respect for human dignity as the image of God (imago Dei). This study contributes to the development of ethical guidelines for churches in utilizing AI responsibly while maintaining theological integrity and Christian spiritual values.

The development of artificial intelligence (AI) has transformed organizational landscapes and driven fundamental changes in leadership practices and strategic management. This article aims to critically examine AI-based leadership by highlighting its opportunities, risks, and implications for strategic management. The study employs a qualitative literature-based approach using an integrative literature review strategy. The data consist of secondary scholarly literature relevant to AI, leadership, governance, innovation, and strategic management, which were analyzed through qualitative thematic analysis and conceptual content analysis. The findings show that AI-based leadership creates opportunities in the form of improved decision quality, faster strategic analysis, operational efficiency, stronger innovation, and enhanced organizational agility. However, AI integration also presents risks, including algorithmic bias, limited decision transparency, technological dependency, accountability challenges, and ethical concerns. This study confirms that AI does not fully replace human leaders; rather, it fosters a hybrid leadership model that requires technical, adaptive, transformational, and ethical capabilities. The study implies that the effectiveness of AI-based leadership depends on responsible governance, organizational cultural readiness, and balanced human–machine collaboration in supporting strategic management.

This study examines the role of digital philanthropy in fostering social solidarity in Indonesia through community-based social actions. The rapid growth of digital philanthropic practices in the post-pandemic era, mediated by social media and online platforms, has transformed collective humanitarian engagement. This study aims to explore how digital philanthropy contributes to the formation of social solidarity and social cohesion. A qualitative approach using a case study method was employed. Data were collected through in-depth interviews with fifteen community-based philanthropic actors, participant observation, and social media document analysis. Data analysis followed Miles and Huberman’s interactive model, including data reduction, data display, and conclusion drawing. The findings reveal that digital philanthropy strengthens social solidarity through digital empathy, trust-building, and collective participation. Social media functions as an inclusive interactive space that expands cross-group solidarity networks. However, challenges such as digital inequality and algorithmic bias remain significant. This study concludes that digital philanthropy holds strategic potential to enhance social solidarity when supported by inclusive and sustainable governance frameworks.

The rapid development of Artificial Intelligence (AI) has brought significant changes to the criminal justice system, particularly in criminal investigations and evidentiary processes, while simultaneously raising complex legal and ethical challenges. Objective: This study aims to analyze the legal implications of the use of AI in criminal investigations, focusing on its benefits, risks, and challenges related to the admissibility of AI-based evidence, as well as the need for regulatory frameworks that ensure fairness, transparency, and accountability. Methods: This research employs a normative qualitative approach through the analysis of legal regulations, a review of legal and technological literature, and a comparative approach across jurisdictions, complemented by case studies of AI applications in law enforcement practices. Results: The findings indicate that AI enhances investigative efficiency through data analysis, crime prediction, and digital forensics; however, it also poses risks such as algorithmic bias, human rights violations, and issues concerning the reliability and transparency of evidence. Furthermore, differences across legal systems result in the absence of uniform standards for the admissibility of AI-based evidence. Therefore, adaptive regulatory frameworks grounded in the principles of fairness, transparency, and accountability are required, along with strengthened human oversight to ensure that the use of AI aligns with the principles of justice and human rights protection.

The retention of customers in the retail banking sector is a critical economic imperative; however, predictive modeling is frequently hindered by severe class imbalance and the “Black Box” nature of complex algorithms. This study proposes a Heterogeneous Stacking Ensemble framework integrating XGBoost, CatBoost, and Random Forest base learners with a Logistic Regression meta-learner to forecast customer attrition. To overcome the pervasive “Majority Class Bias,” we introduce a “Dual-Imbalance Defense” that synergizes the Synthetic Minority Over-sampling Technique (SMOTE) with algorithmic cost-sensitive penalization. Furthermore, moving beyond standard accuracy metrics, the framework mathematically derives a dynamic classification threshold to guarantee a strict 0.90 recall rate, actively optimizing the capture of at-risk capital. Model opacity is addressed through the integration of a SHapley Additive exPlanations (SHAP) TreeExplainer. This cooperative game theory approach provides localized, patient-level “Reason Codes” for regulatory compliance and reveals global systemic vulnerabilities, including non-linear drivers such as the “Product Paradox.” Achieving a 0.90 recall rate and an AUC of 0.8654, this framework provides a statistically robust and operationally transparent tool for targeted customer retention.

This study aims to provide a comprehensive analysis of Predictive Policing and its implications for law enforcement transformation in Indonesia, based on an extensive review of its global applications, benefits, and challenges. The study uses qualitative literature and international case study review methods to assess the impact and complexity of implementing digital technologies such as artificial intelligence (AI), machine learning, and big data analytics within a Predictive Policing framework. The results of this review highlight that while Predictive Policing offers significant potential for proactive crime prevention and increased operational efficiency, its implementation is consistently fraught with critical legal, ethical, and technical challenges, including regulatory gaps, risks of algorithmic bias, and data privacy concerns, which are particularly relevant to Indonesia. The findings underscore that public trust and police legitimacy in the context of adopting such technologies are strongly influenced by transparency, strong accountability mechanisms, and community involvement in shaping their use. This study contributes to the growing discourse on digital policing in developing countries and culminates in practical policy recommendations designed to guide the Indonesian police towards the development and implementation of Predictive Policing models that are effective, efficient, and fundamentally respectful of legal and human rights principles.

Artificial Intelligence (AI) is increasingly recognized as a transformative enabler of early disease detection, with the potential to improve diagnostic accuracy, support predictive risk stratification, and advance preventive healthcare. Despite rapid methodological progress, many existing reviews remain performance-centric, offering limited insight into generalizability, ethical governance, and real-world implementation constraints. This paper presents a narrative and integrative review with an adoption-focused, translational perspective, synthesizing recent developments in AI-driven early disease detection across oncology, cardiology, neurology, and infectious disease surveillance. Drawing on peer-reviewed literature published primarily between 2016 and 2025, the review examines reported performance gains alongside persistent limitations related to data heterogeneity, population bias, explainability, and regulatory fragmentation. Through cross-sectional synthesis, we identify three recurring gaps in prior reviews: (i) overgeneralization of AI’s diagnostic superiority, (ii) insufficient consideration of ethical and legal accountability, and (iii) a lack of actionable guidance for scalable clinical implementation. Integrating technical, ethical, and policy dimensions into a unified conceptual framework, this review demonstrates that while AI systems can consistently enhance diagnostic accuracy and early risk stratification in well-defined tasks, sustained clinical adoption depends on aligning technical performance with governance readiness, interpretability, and workflow integration. The analysis further highlights how implementation mechanisms—such as explainable AI, continuous post-deployment monitoring, and clinician-centered deployment strategies—mediate the translation of algorithmic innovation into real-world healthcare impact. Overall, this review provides a critical reference for researchers, clinicians, and policymakers seeking to translate AI innovation into safe, equitable, and trustworthy clinical practice.

Digital transformation in the public sector has encouraged the adoption of Artificial Intelligence (AI) as a strategic instrument to enhance the effectiveness and quality of public service delivery. In Indonesia, the implementation of AI within the public administrative system remains at an early stage and faces various structural, regulatory, and ethical challenges. This study aims to analyze the opportunities, challenges, and ethical implications of AI implementation in Indonesia’s public administration. The research employs a qualitative approach through literature review and policy analysis of governmental digital transformation regulations. The findings indicate that AI holds significant potential to improve bureaucratic efficiency, service transparency, and data-driven decision-making processes. However, regulatory gaps, limited digital literacy among public officials, the risk of algorithmic bias, and data protection concerns constitute major obstacles to its effective implementation. The novelty of this study lies in integrating public administration management analysis with a public service ethics framework grounded in good governance principles within the context of AI implementation in Indonesia. This study recommends strengthening regulatory frameworks for AI in the public sector, enhancing human resource capacity, and developing ethical guidelines for AI utilization to ensure that public services remain accountable, equitable, and oriented toward the public interest.

The rapid advancement of Artificial Intelligence (AI) is reshaping public governance, including legislative processes. In the United Arab Emirates (UAE), AI is being actively utilized to enhance law-making through faster drafting, improved consistency, and greater transparency. This study examines the role of AI in the UAE’s legislative functions, focusing on how AI tools assist in analyzing legal data, formulating policy recommendations, and drafting legislation. It explores how AI impacts the speed, accuracy, and legitimacy of law-making, while also addressing the ethical and legal challenges of delegating legislative tasks to intelligent systems. Using a qualitative case study method, the paper evaluates government initiatives, expert insights, and regulatory structures that frame AI's integration into the UAE’s law-making system. While AI offers opportunities for data-driven governance and increased legislative productivity, it also presents risks such as algorithmic bias, reduced human oversight, and accountability gaps. The study emphasizes that AI must be governed by strong regulatory frameworks to safeguard democratic values, fairness, and legal integrity. By analyzing a pioneering national model, this research contributes to global discussions on AI in governance and offers key insights for policymakers, technologists, and legal scholars seeking to balance innovation with ethical and legal standards.

This research aims to critically examine the ethical integration of artificial intelligence (AI) in education through the perspective of maqāṣid al-sharīʿah, emphasizing the alignment between technological innovation and Islamic moral principles. The methods used are a systematic literature review and thematic content analysis against peer-reviewed publications for the period 2015–2025, which discuss the application of AI in primary, secondary, and higher education. The study identified dominant ethical issues, such as data privacy, algorithmic bias, accountability, human agency, and moral development, which were then mapped to Islamic ethical goals, including ʿadl (justice), amānah (belief), karāmah al-insān (human dignity), and ḥifẓ al-ʿaql (protection of reason). The results of the analysis show that the adoption of AI in education often emphasizes efficiency, personalization, and predictive analytics, but has the potential to reduce learners' autonomy and ethical reasoning. The mapping of maqāṣid al-sharīʿah shows a strong normative conformity, so that Islamic principles can be a moral foundation as well as a practical guide for AI governance. The research contribution is theoretical by bridging the literature on AI ethics and Islamic educational philosophy, as well as practical by offering an integrative framework for AI policymakers, educators, and developers. The integration of maqāṣid al-sharīʿah in AI governance ensures justice, trust, inclusivity, and the development of the whole human being (insān kāmil).

The use of Artificial Intelligence (AI) in higher education learning has increased significantly, particularly among Informatics Engineering students. Although AI provides various benefits in supporting the learning process, its utilization also raises ethical concerns, especially related to algorithmic bias and responsible use of technology. This study aims to analyze the perceptions of Informatics Engineering students regarding bias and ethics in the use of artificial intelligence in learning. The research employed a quantitative descriptive approach. Data were collected through a Likert-scale questionnaire distributed to 80 Informatics Engineering students who had experience using AI in learning activities. Descriptive statistical analysis was conducted using mean scores and percentages. The results indicate that students demonstrate a high level of ethical awareness and responsibility in using AI; however, their perception of potential bias in AI systems remains at a moderate level. These findings reveal a gap between normative ethical awareness and critical understanding of algorithmic bias. This study recommends strengthening contextual and applied AI ethics literacy within the Informatics Engineering curriculum to promote responsible and ethical use and development of artificial intelligence technologies.

The rapid advancement of artificial intelligence (AI) in the digital age offers substantial benefits by enhancing efficiency and productivity. Nevertheless, these developments also pose significant challenges to the protection of human rights. Issues such as privacy violations, algorithmic bias, discrimination, and opaque automated decision-making highlight the need for a strong integration of ethical values and legal frameworks in the use of AI. This study applies a normative legal method supported by literature-based research to examine the existing regulatory frameworks and the ethical principles underpinning them. The findings indicate that ethical principles such as transparency, accountability, fairness, and human-centeredness serve as essential moral guidelines to prevent AI misuse. Meanwhile, legal rules ensure certainty, establish accountability mechanisms, and provide sanctions for violations. The synergy between ethics and law forms a crucial foundation to ensure that technological innovation aligns with the protection of human rights, upholds human dignity, and supports the creation of a safe and just digital environment

This paper reviews the role of Artificial Intelligence (AI) in Islamic education within secondary schools, emphasizing both its practical uses and the ethical challenges it presents. The review looks into the current trends, tools, and the impact of AI on the learning experience, as well as its ethical implications from an Islamic perspective. The study follows a systematic literature review (SLR) approach based on the PRISMA guidelines and includes research from 2022 to 2025, sourced from platforms like Google Scholar. After a thorough selection process, 15 articles were included in the review, offering valuable insights into the technological and ethical aspects of AI in Islamic secondary education. The use of AI has notably enhanced learning outcomes in Islamic education by allowing personalized learning, boosting student engagement, and streamlining feedback mechanisms. Tools like intelligent tutoring systems and educational chatbots have been widely adopted. However, challenges around data privacy, algorithmic bias, and technology access persist. Additionally, incorporating Islamic ethical values into AI-driven educational platforms presents both opportunities and challenges. Addressing these ethical implications is vital, requiring frameworks that align with Islamic principles such as maṣlaḥa (public welfare), justice, and human dignity. Education policies and teacher training programs should concentrate on promoting the responsible use of AI, ensuring it improves educational experiences while preserving ethical and cultural integrity.

This study presents an ethical framework for learning analytics aimed at addressing key challenges related to the collection and use of student data in higher education. Learning analytics, a powerful tool for improving student outcomes and institutional decision-making, has raised ethical concerns regarding data privacy, transparency, fairness, and equity. The proposed framework integrates four core principles: data privacy, informed consent, transparency, and fairness, ensuring that institutions use learning analytics responsibly while safeguarding student rights. A central feature of the framework is its focus on promoting equitable decision-making, minimizing bias, and preventing the reinforcement of existing inequalities in algorithmic and data-driven decisions. The framework also emphasizes the importance of continuous ethical oversight, holding institutions accountable for ethical data use and adapting practices as technology evolves. The study concludes that the framework offers a comprehensive solution to the ethical challenges in learning analytics, providing institutions with a practical guide to embedding ethical principles in data practices. Additionally, the research discusses its potential to foster fairness, equity, and transparency in decision-making processes. Future research is recommended to refine the framework and explore its application across various educational contexts, ensuring responsible and inclusive use of learning analytics.

The integration of artificial intelligence (AI) in forensic investigation has significantly transformed the analysis and authentication of digital evidence. This paper explores the role of AI technologies, specifically machine learning and deep learning algorithms, in examining digital evidence from various sources, including computers, mobile devices, and network systems. We provide an in-depth analysis of current AI-based forensic tools, their efficiency in evidence authentication, and the challenges they face regarding legal admissibility. Our findings indicate that AI-powered forensic systems can detect digital evidence tampering with 94.7% accuracy, drastically reducing analysis time from weeks to hours. However, challenges remain, particularly in areas such as algorithmic transparency, bias prevention, and ensuring the integrity of the chain of custody. This research offers a framework for incorporating AI in forensic laboratories, while also addressing crucial legal and ethical concerns to ensure the admissibility of AI-analyzed evidence in court. These considerations are essential for the widespread acceptance and use of AI in forensic investigations.

The development of digital information technology has fundamentally transformed the ways university students access and interpret religious information. Social media platforms such as TikTok, Instagram, and YouTube have become primary spaces where students encounter diverse forms of religious content, ranging from educational dakwah to provocative narratives that may trigger tension and conflict. This phenomenon creates both opportunities and challenges for shaping religious moderation, particularly among first-semester students who are in the formative stage of constructing their intellectual and religious identities. This study aims to explore in depth the dynamics of religious moderation among first-semester students at IAINU Tuban within the context of digital polarization. A descriptive qualitative method was employed, utilizing semi-structured interviews, observations, and documentation as data collection techniques. Data were analyzed through thematic processes of reduction, presentation, and conclusion drawing. The findings reveal that students exhibit high social media engagement and are frequently exposed to short dakwah content circulated through algorithmic mechanisms. However, they also confront digital polarization characterized by emotional disputes, informational bias, and negative perceptions of other religious groups. While students demonstrate an initial understanding of religious moderation—such as wisdom, tolerance, and resistance to provocation—they have not fully developed adequate religious digital literacy to critically evaluate extreme content. This research concludes that social media simultaneously serves as a medium for dakwah and a site of polarization, highlighting the need for systematic reinforcement of digital literacy and religious moderation within Islamic higher education. 

Digital transformation has significantly reshaped human resource management (HRM) through the adoption of Human Resource Information Systems (HRIS), artificial intelligence (AI), big data analytics, e-learning platforms, and remote work technologies. Although these innovations improve efficiency and decision-making, they also generate ethical challenges related to data privacy, algorithmic bias, transparency, and employee monitoring. This article examines the role of professional ethics in HRM within the context of digital transformation, highlighting both emerging challenges and potential opportunities. This study employs a conceptual research approach supported by a comprehensive literature review of scholarly works on HRM, professional ethics, and digitalization. The analysis focuses on core ethical principles such as integrity, fairness, responsibility, professionalism, and confidentiality, and evaluates their implementation in digital HR practices. The findings indicate that unethical use of digital technologies may lead to discrimination, reduced employee trust, and violations of individual rights, particularly through biased AI-based recruitment systems and opaque performance evaluation mechanisms. However, digital transformation also offers opportunities to strengthen ethical HR governance. The use of ethical data management, algorithmic audits, digital transparency, and e-learning-based ethics training can enhance accountability and fairness in HR processes. The study concludes that integrating professional ethics with digital HRM is essential for developing human-centered, sustainable, and trustworthy organizations in the digital era.

This study presents a Systematic Literature Review on Artificial Intelligence (AI) and Natural Language Processing (NLP) applications for customer support automation and digital service optimization. The review follows the PRISMA framework to ensure methodological rigor and transparency, focusing on literature published between 2020 and 2025 from the Scopus database. The findings reveal that AI-driven technologies, including Machine Learning, Deep Learning, and Large Language Models, have significantly improved efficiency, response time, and customer satisfaction in customer support and digital service. Common NLP applications include sentiment analysis, ticket classification, and automated response generation. Among these, hybrid and transformer-based models demonstrate superior accuracy and contextual understanding compared to traditional algorithms. However, several challenges persist, including data quality limitations, privacy and security concerns, algorithmic bias, and linguistic ambiguities such as sarcasm and negation. Moreover, issues related to trust and ethical adoption continue to influence user acceptance of AI systems. This review provides a comprehensive synthesis of current methodologies, trends, and research gaps, offering insights for future studies to develop explainable, secure, and human-centered AI systems that enhance the sustainability and transparency of digital customer support services.

The rapid growth of social media as a primary channel for information dissemination has triggered a significant surge in the distribution of hoaxes, potentially damaging social order, instigating mass disinformation, and threatening national security. This research aims to design an intelligent algorithm for hoax detection by integrating a critical thinking approach into Natural Language Processing (NLP)-based text processing. The algorithmic model is built using a combination of linguistic features, argument logic, and cognitive indicators such as the detection of unsubstantiated claims, identification of source bias, and evidence testing. To ensure accountability and transparency of the system, an Explainable AI (XAI) approach is applied so that classification results can be understood by non-technical users. The research results show that integrating critical thinking significantly improves detection accuracy to 93.1%, with an increase in precision and recall for detecting hoaxes based on emotional narratives. Beyond technical aspects, this model aligns with the mandate of Law of the Republic of Indonesia Number 11 of 2008 concerning Information and Electronic Transactions (ITE Law), particularly Article 28 paragraph (1), which prohibits the dissemination of false and misleading news that harms the public. Therefore, this system is not only scientifically relevant but also supports law enforcement and strengthens digital literacy in the post-truth era. These findings are expected to be a strategic contribution to the development of an ethical, critical, and responsible digital ecosystem.