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The development of Artificial Intelligence (AI) has transformed how business organizations manage operations, process information, and make strategic decisions, thereby creating a need for leadership patterns that are more adaptive, visionary, and responsive to digital transformation. This study aims to explain the concept of AI Leadership in the business context, identify why modern leadership must adapt to the advancement of AI, and analyze the leadership strategies that are relevant for navigating the era of AI-driven business. The study employs a Systematic Literature Review (SLR) approach with a narrative-thematic synthesis of relevant open-access scholarly literature on AI Leadership, digital leadership, human–AI collaboration, business transformation, and AI ethics. The findings show that AI Leadership represents a modern form of leadership that emphasizes not only technological understanding but also the ability of leaders to direct organizational change, foster data-driven decision making, manage collaboration between humans and technology, and implement responsible AI governance. The study also finds that the main strategies required by leaders in the AI era include strengthening digital literacy, developing adaptive managerial capabilities, enhancing human–AI collaboration, and integrating ethical principles into technology implementation. This article contributes conceptually by reinforcing AI Leadership as a leadership framework that is highly relevant to modern organizations and offers practical implications for the development of business leadership in the era of digital transformation.

This study examines the application of Transparent Artificial Intelligence (AI) for fraud detection in public welfare programs using publicly available administrative data. Persistent challenges in welfare governance such as misallocation, fraud, and data inaccuracy necessitate analytical frameworks that are both effective and explainable. The research aims to design and evaluate an interpretable anomaly detection system capable of identifying irregularities in welfare distribution while maintaining transparency and accountability. Methodologically, the study employs two unsupervised models Isolation Forest and Local Outlier Factor (LOF) to detect anomalies in sub-district-level welfare data, incorporating features such as population size, number of beneficiaries, and coverage ratio. An Explainable AI (XAI) framework integrating surrogate Random Forests, Permutation Feature Importance (PFI), and local linear surrogates (LIME-like) is applied to ensure interpretability of both global and local model behaviors. Findings reveal that receivers per 1000 population and percentage coverage are dominant determinants of anomaly scores. Fifteen administrative units were flagged for potential inconsistencies suggesting over- or under-reporting of beneficiaries. Cross-validation between IF and LOF models confirmed consistency in identifying anomalous regions. The integrated XAI explanations enhance transparency, enabling policymakers and auditors to trace the rationale behind detected anomalies. In conclusion, the proposed Transparent AI framework demonstrates that combining anomaly detection with interpretability tools can strengthen accountability and fairness in welfare administration. It offers a reproducible, ethical, and data-driven approach to social program monitoring, reinforcing public trust and supporting responsible AI governance.

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.

The development of Artificial Intelligence (AI) technology has significantly changed strategy and decision making in marketing management. However, the massive use of AI raises new challenges regarding ethics, transparency and governance. This research aims to analyze the impact of using AI, especially recommender systems and large language models (LLMs), on the effectiveness of marketing decisions, as well as the role of AI governance in controlling emerging ethical issues. The research method uses a quantitative approach with Structural Equation Modeling (SEM) analysis of data collected from 250 marketing professionals in Indonesia. The research results show that the use of AI has a significant positive effect on the effectiveness of marketing decisions (β=0.62, p<0.001), but also raises ethical issues (β=0.48, p<0.01). Ethical issues were proven to reduce the effectiveness of marketing decisions (β=-0.31, p<0.05), while good AI governance was able to moderate the negative impact of ethical issues (β=0.27, p<0.05). These findings underscore the importance of AI governance in building effective and ethical marketing systems.

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).

Abstract: The rapid diffusion of Generative Artificial Intelligence (AI) has intensified public debate regarding its benefits, risks, and societal implications. This study investigates public sentiment and thematic structures surrounding Generative AI by analyzing Twitter discourse as a representation of large-scale, real-time public perception. The research addresses two main problems: how public sentiment toward Generative AI is distributed and what dominant themes shape this perception. Accordingly, the objective is to map both emotional polarity and thematic narratives embedded in social media conversations. A computational mixed-methods approach was employed using a dataset of 12,470 tweets collected on 17 December 2024. Sentiment classification was conducted using a transformer-based DistilBERT model, while semantic representations were generated with Sentence-BERT. Topic modeling was performed using BERTopic, integrating HDBSCAN clustering and class-based TF-IDF to extract coherent and interpretable topics. Human-in-the-loop validation supported the interpretive robustness of topic labeling. The findings reveal that public sentiment toward Generative AI is predominantly positive (41.8%), particularly in relation to productivity enhancement, education, and creative applications. Neutral sentiment (31.4%) reflects informational discourse, while negative sentiment (26.8%) centers on ethical concerns, privacy risks, misinformation, and AI hallucinations. Seven dominant topics were identified, with clear topic–sentiment alignment showing optimism in utility-driven themes and skepticism in ethics- and risk-related discussions. In conclusion, public perception of Generative AI is dualistic—characterized by strong enthusiasm alongside persistent caution. These results provide empirical insights for AI governance, responsible innovation, and future research on socio-technical impacts of Generative AI. *    

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 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.