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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 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. 

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.

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.

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.

This article explores the background, methods, results, and conclusions of digitalization in public services, focusing on its impact on the social administration ecology. It examines the rapid adoption of e- government and ICT (information and communication technology) by public administrations worldwide since 2020, investigating both opportunities and risks. Methods include a systematic literature review and qualitative case studies drawn primarily from peer-reviewed journals published between 2020 and 2024. The results show that digitalization in public services offers significant benefits: improved efficiency, transparency, citizen participation, reduced corruption, and enhanced environmental governance. However, it also presents risks, in particular widening digital divides, loss of human aspects in administrative interaction, ethical concerns (data privacy, algorithmic bias), regulatory and infrastructural challenges, and potential exclusion of marginalized groups. The discussion elaborates on how these opportunities and risks reshape the ecology of social administration defined here as the interplay of structures, actors, norms, technologies, and environment in public administration. In conclusion, the paper argues that digitalization must be managed with attention to equity, ethical governance, infrastructure readiness, and regulatory safeguards. Key recommendations include fostering digital literacy, inclusive design, transparency in data and algorithmic processes, and participatory governance.  

The advent of ChatGPT, a generative AI technology, has initiated significant transformation within the finance sector by allowing users to engage with digital systems using natural language. Despite its promising capabilities, integrating ChatGPT into financial operations introduces a host of ethical concerns that must be rigorously addressed to ensure its appropriate and conscientious use. This policy-focused article begins with a brief overview of ChatGPT’s utility in financial contexts and then examines the ethical dilemmas it raises. These include biased decision-making outputs, the risk of misinformation influencing financial outcomes, data privacy and security vulnerabilities, opacity in algorithmic processes, the displacement of human workers, and complex legal implications. We argue that financial entities adopting ChatGPT have a responsibility to develop and implement comprehensive strategies aimed at mitigating these ethical risks. In support of this goal, we outline policy recommendations designed to directly address these pressing issues. Ultimately, this article emphasizes the urgent need for a robust ethical framework to guide the deployment of ChatGPT in financial environments, ensuring that its implementation benefits both individuals and society. Furthermore, we highlight key areas for future research that can support ongoing efforts to integrate AI responsibly in finance.

This study examines the role of perception in individual and organizational decision-making through a Systematic Literature Review (SLR) and content analysis approach. Perception is understood as an internal process that shapes understanding of objects, influencing preferences and decisions. Contextual factors such as culture, socio-economics, and technology also contribute to shaping perception. This study adopts a theoretical framework in which perception mediates the relationship between external attributes—such as price, quality, and brand image—and final decisions. By analyzing 993 scholarly articles from 2021 to 2025, this research maps perception models in consumer and organizational domains and identifies research gaps related to the integration of digital factors such as algorithms and AI. The validation process was conducted through triangulation between AI-generated prompts and human-based thematic analysis. The results highlight the need for perception models to adapt to digital dynamics and emphasize the importance of ethical integration in modern decision-making systems.  

Artificial Intelligence (AI) is quickly becoming a game changer in the way businesses build and manage their strategies. This article explores how AI is helping organizations make faster and smarter decisions, streamline operations, and spark innovation across various industries. With the ability to process massive amounts of data, AI tools can uncover valuable insights about market trends and customer behavior, allowing companies to respond more accurately and stay ahead of the competition. From machine learning and generative AI to natural language processing and digital twins, these technologies are transforming everything from internal workflows to how businesses connect with customers. The article also offers a practical roadmap for adopting AI in a business setting, covering steps like evaluating readiness, running pilot projects, and measuring success through return on investment (ROI). It emphasizes the need for strong data infrastructure, skilled teams, and a culture that supports innovation and data-driven thinking. Challenges such as algorithmic bias, data privacy, and internal resistance to change are also addressed. Real-world examples from banking, retail, and manufacturing show how AI can deliver real impact improving efficiency, increasing customer satisfaction, and driving business growth. Ultimately, embracing AI isn’t just about keeping up with technology it’s about shaping the future of smart, strategic, and ethical business.

Teachers are under increasing pressure to deliver personalized, standards-aligned instruction while managing time constraints and rising workloads. Traditional lesson planning often limits creativity and adaptability due to its complexity and repetitive demands. In response, Artificial Intelligence (AI) has emerged as a promising tool to support instructional planning. This study highlights how AI enhances teacher efficiency, simplifies administrative tasks, and supports differentiated, data-driven instruction. However, these benefits require thoughtful and responsible integration. AI adoption must include safeguards for data privacy, ensure algorithmic transparency so teachers understand the basis of system recommendations, and actively mitigate systemic bias that may disadvantage certain learner groups. Most importantly, teachers should remain actively involved in reviewing and adapting AI-generated content to preserve professional judgment and uphold pedagogical integrity.

The standard of proof is the foundation that keeps the criminal justice system from turning into a repressive tool to prevent state power from imposing arbitrary sentences. However, in the midst of technological developments, it has created an unstable evidence landscape that threatens the continuity of the legitimacy of the law itself without a standard of proof that can be objectively verified. This study uses a qualitative research method with a conceptual approach. The data collection method is collected using literature studies, then analyzed using qualitative methods and presented descriptively. The results of the study show that maintaining the existence of the standard of proof is no longer adequate if it is not accompanied by a responsible and adaptive reformulation to the complexity of contemporary evidence. Exploration of alternative forms of evidence offers opportunities to increase precision and transparency that have been difficult to achieve with traditional mechanisms, but all of these innovations can only contribute constructively if they are placed within a strict legal and ethical framework, given the inherent risks such as algorithmic bias, the reduction of judicial convictions to statistics without context, and gaps in accountability between jurisdictions in the application of forensic technology.

The rapid advancement and integration of Artificial Intelligence (AI) into diverse sectors of society have generated complex ethical and human rights challenges. Technologies involving surveillance, data collection, algorithmic decision-making, and facial recognition pose significant risks to privacy, equality, and freedom of expression. This study examines the intersection of AI and human rights through a comparative analysis of regulatory frameworks in the European Union (EU), the United States (US), and Asia. Employing a comparative legal approach, the research analyzes international and national regulatory instruments, including the EU AI Act, the General Data Protection Regulation (GDPR), and China’s Personal Information Protection Law (PIPL). Case studies of AI-related human rights violations, such as algorithmic bias and discrimination, are incorporated to illustrate real-world implications. Findings reveal substantial differences in governance approaches: the EU emphasizes a risk-based model prioritizing human rights protections, while the US and Asia adopt more fragmented or centralized strategies. The study underscores the urgent need for global regulatory harmonization to safeguard fundamental rights and promote ethical AI development. By highlighting both strengths and limitations of existing frameworks, the research contributes to ongoing debates on balancing innovation with accountability, transparency, and human rights protection in the digital era.

The rapid adoption of algorithmic systems in public governance has transformed decision-making and service delivery, offering promises of efficiency and transparency. Yet, these technologies raise pressing concerns regarding fairness, bias, and social justice. This study investigates the intersection of digital governance, algorithmic decision-making, and social justice, with particular emphasis on emerging democracies. Employing a qualitative socio-legal approach, the research combines normative analysis of governance regulations, case studies of algorithmic applications in public administration, and interviews with policymakers and technology law experts. Comparative analysis across emerging democracies highlights diverse strategies for addressing equity concerns in algorithmic systems. Findings reveal that while algorithmic systems enhance efficiency, they often reinforce existing inequalities due to insufficient safeguards against bias and discrimination. Moreover, regulatory frameworks remain fragmented and inadequate to ensure fairness and accountability. The study proposes the development of adaptive legal frameworks that integrate transparency, accountability, and citizen engagement into AI governance. By embedding social justice principles into algorithmic regulation, governments can foster inclusive policy design and equitable outcomes. This research contributes to ongoing debates on balancing technological innovation with democratic values, emphasizing the need for governance models that prioritize fairness alongside efficiency.

The development of digital technology has changed many aspects of people's lives, including in terms of social justice. On the one hand, digitalization opens up access to information and accelerates global connectivity. However, on the other hand, various serious challenges have emerged in law enforcement. Inequality in access to technology, algorithmic bias, and the rise of cybercrime are the main issues that test the resilience of the current legal system. This paper aims to explore how the law can play an effective and responsive role in maintaining social justice in the digital era. With a qualitative approach based on literature studies, this article presents an analysis of various current academic literature discussing law, social justice, and technology. The results show that the legal system must be able to adapt quickly, not only at the regulatory level, but also in institutional structures and community participation. The role of society is key to building inclusive and sustainable digital justice. Therefore, law in the digital era is not enough to be just a set of rules, but must be a tool for social empowerment that is able to respond to the dynamics of the times without abandoning the values ​​of justice. Collaboration between stakeholders and strengthening digital literacy are important elements in creating a digital space that is fair for all.

Artificial Intelligence (AI) is increasingly used in financial accounting to improve decision-making effectiveness. This research analyzes the role of AI in supporting data-driven decision making and identifies challenges in its implementation. Using a qualitative approach with the Systematic Literature Review (SLR) method, this study reviewed 41 relevant articles from national and international journals. The results showed that 28 studies supported the effectiveness of AI in improving financial decision-making by automating transaction recording, enabling algorithm-based predictive analysis, and detecting financial anomalies. AI enables companies to respond faster to market changes, increase transparency of financial reports, and reduce human errors in accounting processes.However, 13 studies highlighted challenges such as technological complexity, limited transparency in decision-making, algorithmic bias, and organizational readiness. In addition, evolving regulations are an obstacle to ensuring optimal use of AI while minimizing ethical and legal risks. The success of AI in financial decision-making depends on infrastructure readiness, regulatory support, and human resource competencies. Without a well-planned strategy, AI may pose new challenges that hinder its effectiveness. Therefore, this study provides insights into the optimal AI implementation strategy to ensure that this technology improves the accuracy and transparency of decision making while maintaining financial accounting accountability.

The increasing adoption of artificial intelligence (AI) in decision-making processes has raised significant concerns regarding algorithmic bias and legal accountability. This study examines the regulatory challenges and enforcement gaps in addressing AI bias, with a particular focus on Indonesia’s legal landscape. Through a comparative analysis of AI governance frameworks in the European Union, the United States, China, and Indonesia, this research identifies key deficiencies in Indonesia’s regulatory approach. Unlike the EU’s AI Act, which incorporates risk-based classification and strict compliance measures, Indonesia lacks a dedicated AI legal framework, leading to limited enforcement mechanisms and unclear liability provisions.The findings highlight that transparency mandates alone are insufficient in mitigating algorithmic discrimination, as weak enforcement structures hinder effective regulatory oversight. Furthermore, the study challenges the notion that global AI regulatory harmonization is universally applicable, emphasizing the need for a context-sensitive hybrid model tailored to Indonesia’s socio-legal environment. The research suggests that Indonesia must adopt a comprehensive AI legal framework, strengthen regulatory institutions, and promote interdisciplinary collaboration between legal experts and AI developers. Future research should focus on empirical case studies, the development of context-specific AI accountability models, and the role of public engagement in AI bias mitigation. These efforts will be essential in shaping effective AI governance strategies that ensure fairness, transparency, and accountability in Indonesia’s digital transformation.

The proliferation of network information algorithms (NIAs) in contemporary society has sparked significant ethical concerns regarding their societal impact. This study investigates the influence of NIAs on social interactions, decision-making processes, and the perpetuation of structural biases through a multidisciplinary perspective (Ananny, 2023). The findings reveal that while NIAs enhance operational efficiency across various domains, they also introduce ethical challenges, including privacy infringements, systemic inequities, and algorithmic opacity, which threaten social justice. Employing Ananny’s (2023) conceptual framework—which categorizes NIAs into three dimensions: encounters, observation, and probability/temporality—this research deconstructs the operational mechanisms of these algorithms. The analysis demonstrates that NIAs not only replicate historical biases but also engender new forms of discrimination through ostensibly neutral predictive processes. For example, algorithm-driven recruitment systems may perpetuate gender disparities if their training data reflects prior discriminatory practices (Crawford, 2021). This study underscores the inextricable link between technological ethics and societal context, arguing that an overreliance on algorithmic systems risks undermining human autonomy (Zuboff, 2019). The originality of this research lies in its integration of computational ethics theory with empirical case studies, such as the deployment of NIAs in mass surveillance, where privacy is often compromised in pursuit of perceived security. To ensure academic rigor, the arguments are developed through a critical comparison with prior research (e.g., Mittelstadt et al., 2016), while avoiding redundancy in phrasing or structure. Scholars such as Floridi (2019) emphasize the necessity of algorithmic transparency in regulatory frameworks. However, critics like Noble (2018) argue that technical solutions alone are inadequate; structural reforms in data governance and corporate accountability are essential to mitigate the misuse of NIAs. In response, this study proposes an ethical framework that not only addresses technical risk mitigation but also incorporates civic participation in algorithmic decision-making processes. The ethical implications of NIAs necessitate a holistic approach that integrates principles of data justice, independent algorithmic auditing, and public digital literacy. Future research should explore inclusive models of algorithmic governance, particularly in developing nations where regulatory frameworks often lag behind technological advancements. This study concludes with a reflective inquiry: How can algorithmic accountability be ensured if developers lack transparency regarding data sources and programming logic? By addressing these questions, this research contributes to the ongoing discourse on the ethical governance of NIAs and their societal implications.