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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 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 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 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.

This article explores the application of Islamic ethics, particularly the concept of tawhid, in the development of digital technologies and artificial intelligence (AI). The study employs a qualitative descriptive method based on a literature review to analyze Islamic principles, such as justice (al-'adl), trust (amanah), and public benefit (al-maslahah), in addressing ethical challenges posed by AI. The findings indicate that Islamic values can provide a holistic framework for tackling algorithmic bias, privacy concerns, and social inequalities associated with AI. However, the study identifies a gap in academic research regarding the integration of tawhid principles with AI development. A limitation of this research is the lack of empirical case studies to test the effectiveness of implementing these principles in technology development.

The development of Artificial Intelligence (AI) has brought significant changes to the field of translation, offering ease and efficiency in the translation process. However, its use raises various ethical issues that need to be regulated through a clear code of ethics. This study discusses the Code of Ethics for the Use of AI in Translation, encompassing key principles such as accountability, transparency, privacy, fairness, and cultural respect.The code of ethics aims to optimize AI's potential while minimizing  negative impacts, such as algorithmic bias, loss of cultural meaning in translations, and the declining role of human translators. Through literature reviews and case studies, this research recommends steps such as regular AI system audits, human-AI collaboration, and compliance with data privacy laws. By implementing this code of ethics, the use of AI in translation is expected to operate responsibly, support linguistic diversity, and uphold professional standards in the field of translation.

In the era of artificial intelligence (AI), technology is growing rapidly and changing the way businesses are run. AI enables automation, big data analysis, and increased operational efficiency. However, this development also raises various ethical issues that companies need to pay attention to so as not to harm consumers, society, and the environment. This study explores the paradigm of artificial intelligence in the context of business ethics, focusing on its impacts, challenges, and potential benefits in the business world. This study uses a systematic literature review approach from various reputable sources. The article criteria include literature published from 2019 to 2024 with the theme of business ethics in the AI era, and other articles relevant to this study. Based on the search, 15 articles were determined that were relevant to the predetermined criteria. The results of the study found that in this study, AI is not only a technical tool to improve operational efficiency, but is also able to redefine business ethics in the modern era. AI offers great potential to overcome traditional ethical challenges and create new paradigms in business decision making. However, this opportunity is also accompanied by significant challenges, such as algorithmic bias, data privacy, and lack of regulation. This discussion will review the role of AI in business ethics, the benefits it brings, the challenges it faces, and the steps that can be taken to optimize the ethical use of AI.

This study examines the application of Immanuel Kant's ethical principles in the use of autonomous technology based on Artificial Intelligence (AI) in the academic domain. AI technology has significantly impacted academic efficiency and innovation but has also raised serious ethical challenges, such as algorithmic bias, dependency on technology, and threats to privacy and transparency. Using a Kantian ethical approach, this research identifies key issues and proposes an ethical framework that places justice, transparency, and respect for human dignity at the core of the design and implementation of autonomous technologies. The findings indicate that algorithmic bias can exacerbate academic inequities, while excessive reliance on AI risks diminishing the critical and creative capabilities of academics. Additionally, privacy and accountability in the management of academic data emerge as major concerns requiring ethically grounded policy interventions. This study also highlights the need for a new paradigm ensuring that autonomous technologies uphold humanistic values in academic settings, such as inclusivity, intellectual autonomy, and justice.

Artificial Intelligence (AI) has increasingly become an integral component of modern education, offering significant opportunities to improve accessibility, efficiency, and personalized learning. This study explores students’ perceptions of AI in educational contexts, focusing not only on its practical benefits but also on ethical concerns, particularly regarding data privacy and social responsibility. Adopting a mixed-method design, the research collected data through surveys and classroom observations. The survey responses were analyzed using descriptive statistics to identify trends in perceptions, while observation notes were thematically coded to capture students’ real interactions with AI applications. The findings reveal that students generally exhibit a positive attitude toward AI, recognizing its potential to enhance learning experiences by making education more accessible and efficient. However, the study also highlights that concerns over data privacy and the potential misuse of information remain central issues. Moreover, a notable gap exists between students’ appreciation of the practical benefits of AI and their awareness of ethical dimensions such as algorithmic bias and digital responsibility. This imbalance suggests that while students are ready to embrace AI as a tool, they may not yet be adequately prepared to address the broader ethical implications of its use. The discussion situates these findings within the framework of digital ethics, emphasizing the need for education systems to integrate ethical literacy alongside technological skills. By doing so, schools and universities can ensure that students are both technologically competent and socially responsible. The study concludes that although AI is welcomed in educational settings, greater emphasis on digital ethics education is essential to maximize its benefits and minimize potential risks. This research provides a basis for future studies on policy development and ethical practices for AI in the classroom.

The rapid integration of Artificial Intelligence (AI) in education presents both opportunities and ethical challenges, especially for faith based institutions such as Christian schools. These institutions must balance technological innovation with their theological commitments. As AI becomes more embedded in educational environments, Christian educators face the challenge of integrating AI in a way that aligns with Christian values, human dignity, and relational teaching roles. This study explores how Christian moral teachings can guide the integration of AI into education, providing a framework for ethical AI use in Christian schools. Previous studies have highlighted the ethical concerns associated with AI, including algorithmic bias, data privacy, and the potential for AI to undermine relational teaching roles. Christian educational theology, which emphasizes values such as human dignity, justice, and fairness, offers a robust framework for addressing these concerns. A biblical worldview and the concept of Imago Dei (the image of God) provide theological foundations for integrating AI ethically in education, ensuring that AI tools enhance human centered learning rather than replace human educators. Additionally, literature suggests that ethical guidelines informed by Christian teachings can mitigate AI's ethical risks and promote a more inclusive and equitable educational environment. This study employs a mixed methods approach, combining qualitative and quantitative research methods. The qualitative phase involves document analysis and interviews with Christian educators and theologians to explore theological reflections on AI and its ethical implications.

The rapid adoption of artificial intelligence (AI) in e-commerce is revolutionizing the business landscape. This study explores the rise of AI-powered e-commerce and its impact on business models, strategies, and market dynamics in 2024. Through a multiple case study approach, focusing on leading e-commerce companies such as Amazon, Alibaba, and Shopee, the research reveals that AI is fundamentally transforming the way businesses operate in the digital economy. AI enables enhanced personalization, operational efficiency, and improved customer experiences, driving the emergence of new business models and competitive advantages. However, the adoption of AI also creates significant challenges, including implications for the workforce, ethical concerns surrounding data privacy and algorithmic bias, and potential impacts on market dynamics and competition. The study highlights the need for a strategic and ethical approach to AI adoption, collaboration among stakeholders, and adaptive regulatory frameworks. It concludes with recommendations for businesses, policymakers, and future research to navigate the transformative impact of AI in e-commerce. The findings contribute to the literature on digital transformation and disruptive innovation, offering valuable insights for managers, practitioners, and researchers.

The rapid advancements in Artificial Intelligence (AI) have raised significant ethical concerns across various sectors, necessitating the need for robust ethical frameworks to guide their development and implementation. This study explores the intersection of AI ethics and Islamic law, focusing on how Maqāṣid al-Sharīʿah, the higher objectives of Islamic law, can be applied to AI governance. By examining key Islamic principles such as justice, transparency, privacy, and human dignity, the study investigates how these values can provide a moral compass for addressing AI-related ethical challenges, such as algorithmic bias, privacy violations, and the erosion of human autonomy. The Maqāṣid al-Sharīʿah framework offers a proactive and vision-oriented approach, prioritizing societal well-being while ensuring the alignment of AI technologies with Islamic moral standards. Unlike traditional Islamic legal responses, which are often reactive and case-specific, the Maqāṣid approach promotes the anticipatory evaluation of technologies, emphasizing the need for a balance between technological innovation and ethical responsibility. The paper also discusses potential solutions to bridge the gaps between global AI ethics frameworks and Islamic ethical standards, including interdisciplinary collaboration and the development of hybrid regulatory models. Additionally, it highlights the need for continuous updates to Islamic legal frameworks to address emerging technological issues, ensuring that AI systems are ethically sound, Shariah-compliant, and beneficial to society. This study aims to contribute to the growing discourse on the ethical implications of AI from an Islamic perspective, offering insights into how Islamic law can play a crucial role in shaping the future of AI governance.