Artificial Intelligence and Islamic Jurisprudence: A Critical Analysis of Legal and Ethical Challenges in Automated Decision-Making
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.012 | 0.119 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".