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All Journal Journal of Islamic Law and Legal Studies TechComp Innovations: Journal of Computer Science and Technology
Azizli, Kamran
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Religion Automotive Engineering Computer Science & IT Decision Sciences, Operations Research & Management Law, Crime, Criminology & Criminal Justice

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Artificial Intelligence and Islamic Jurisprudence: A Critical Analysis of Legal and Ethical Challenges in Automated Decision-Making Azizli, Kamran; Gargari, Esmira Hajiyeva; Muchtar, Abdul Haris; Sahal, Abdurrohman
Journal of Islamic Law and Legal Studies Vol 2 No 2 (2025): Journal of Islamic Law and Legal Studies
Publisher : Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/jills.v2i2.117

Abstract

This study critically reassesses Islamic economic law within the rapidly expanding digital economy, emphasizing the necessity of a globally coherent Sharia-compliant regulatory architecture. Using a qualitative library research method, the paper draws from classical jurisprudence, contemporary fintech literature, and international Sharia standards to examine the tensions emerging from technological innovations such as artificial intelligence, blockchain, digital assets, and Islamic fintech platforms. Findings reveal significant regulatory fragmentation across Muslim jurisdictions, inconsistencies in Sharia interpretation, and gaps in digital literacy, which collectively hinder harmonized governance. Moreover, emerging digital financial instruments raise pressing ethical concerns related to transparency, algorithmic bias, cybersecurity, and compliance with prohibitions against riba, gharar, and maysir. The study argues that Maqasid al-Shariah—particularly the principles of ḥifẓ al-māl, maslahah, and harm prevention—provides a holistic framework for balancing innovation with ethical integrity. It also identifies the urgent need for cross-border regulatory harmonization, AI ethics protocols, enhanced Sharia governance structures, and tailored regulatory sandboxes for Islamic fintech. Ultimately, the research offers a conceptual foundation for constructing a future-ready, inclusive, and ethically resilient global Islamic digital finance system.
A Self-Supervised Multi-Scale Fusion Framework for Accurate and Efficient Image Segmentation Yusifova, Elmira Haci; Osmanov, Fuad Fazil; Azizov, Elman; Azizli, Kamran
TechComp Innovations: Journal of Computer Science and Technology Vol. 2 No. 2 (2025): TechComp Innovations: Journal of Computer Science and Technology
Publisher : Pusat Riset dan Inovasi Nasional Mabadi Iqtishad Al Islami

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70063/techcompinnovations.v2i2.125

Abstract

This study conceptually examines a self-supervised multi-scale fusion framework designed to enhance accuracy and computational efficiency in medical image segmentation, a domain where data scarcity and annotation cost remain major challenges. Traditional supervised approaches are constrained by their reliance on extensive labeled datasets, limiting applicability in real-world clinical environments. Self-supervised learning (SSL) mitigates this issue by extracting supervisory signals directly from unlabeled data, enabling the model to learn rich feature representations without human annotation. Simultaneously, multi-scale fusion architectures integrate global contextual information with fine-grained local features, supporting robust segmentation across varying anatomical structures and image resolutions. Through a qualitative methodology involving library research and content analysis, this study synthesizes state-of-the-art SSL-driven segmentation techniques and highlights how adaptive multi-scale fusion mechanisms address limitations of existing convolutional and transformer-based architectures. The analysis indicates that combining SSL and multi-scale strategies leads to more generalizable, scalable, and computationally efficient segmentation pipelines suitable for diverse medical imaging modalities. The proposed framework represents a promising direction for developing next-generation diagnostic tools capable of handling sparse labels, complex textures, and real-time deployment constraints.
Co-Authors Azizov, Elman Gargari, Esmira Hajiyeva Muchtar, Abdul Haris Osmanov, Fuad Fazil Sahal, Abdurrohman Yusifova, Elmira Haci