By Ezieddin Elmahjub

1. Introduction

Artificial Intelligence (AI), particularly large language models (LLMs) and generative AI, is poised to revolutionize how we approach and interact with Islamic jurisprudence. However, the varied and often unorganized Islamic data sources make this shift challenging. From manuscripts and printed texts to audio lectures and other multimedia formats, the materials that undergird Islamic sources often remain inconsistent, unstructured, and difficult to organize. While digitization marks an important step forward, it does not automatically guarantee that these resources are usable for AI-driven applications. For AI to meaningfully assist in complex areas like fatwā issuance and legal interpretation, a robust foundation of well-organized and accessible Islamic data is indispensable. This foundation is essential for the scalability, reliability, and contextual depth of AI-driven tools.

In this essay, I argue that AI has enormous potential to reshape Islamic jurisprudence. Not only can AI speed up legal interpretation, but it can also generate new scholarly insights, facilitate detailed analyses of complex legal texts, and expand comparative jurisprudence by uncovering subtle connections across diverse legal traditions and opinions. However, I also highlight four key challenges that hinder AI’s effectiveness: (1) the difference in Arabic versus English data availability, (2) incomplete digitized collections, (3) complexities in unstructured data processing, and (4) the ethical and methodological demands of fatwā issuance.

In the sections that follow, the essay will first show the promise of AI in Islamic jurisprudence by sharing practical examples, then discuss the challenges in structuring Islamic data sources, and finally explore the future of AI-assisted Islamic legal analysis. This approach aims to provide a clear roadmap for integrating advanced computational tools with the rich traditions of Islamic legal scholarship in a manner that is both innovative and faithful to its established principles.

2. The Promise of AI in Islamic Jurisprudence

AI-assisted jurisprudence uses technologies like LLMs to streamline legal interpretation, facilitate more efficient access to jurisprudential texts, and enable comparative analyses across multiple schools of thought. These emerging tools hold transformative potential for Islamic scholars, who can now pair traditional methods with computational approaches that simplify complex legal questions and expedite source retrieval. Consider this hypothetical scenario of a jurist in Cairo who, using an AI-powered jurisprudence chatbot, can cross-reference rulings from multiple madhāhib in minutes—a task that would have taken much more time using traditional methods. Yet, the same tool struggles to interpret a 14th-century manuscript due to its cursive script and degraded condition. This duality highlights both the promise and the limitations of AI in Islamic jurisprudence.

In practice, early-stage initiatives illustrate the promise of AI assisted jurisprudence. Fatwā ChatBot, for instance, despite employing relatively basic AI techniques, serves as a readily accessible, cost-effective tool for addressing the high volume of frequently asked questions on Islamic jurisprudence, freeing up expert time for complex inquiries.[1] Likewise, Mufti.ai demonstrates the use of a Retrieval-Augmented Generation (RAG) framework to generate answers based on transcripts of Islamic lectures, showing potential for integrating diverse scholarly perspectives despite limitations imposed by its dataset size and technology.[2] More recently, the Usul.ai initiative has taken a notable step forward by training advanced AI models on a digital library of over 8,000 foundational Islamic legal texts.[3] Usul.ai integrates LLMs architecture to enhance search capabilities and provide nuanced text analysis. This integration addresses a frequent challenge in traditional scholarship where materials are often available only in static PDF formats, thus slowing down research and limiting accessibility. Notably as well, earlier foundational efforts from institutions like NYU Abu Dhabi’s CAMeL Lab, which employs AI to continually refine its digital language tools, help build the infrastructure that makes such innovations possible.[4]

Imagine a future scenario in which AI empowers researchers to perform complex analytical tasks currently requiring extensive manual effort; enabling, for example, instantaneous cross-referencing of legal reasoned opinions across multiple schools of thought or the rapid identification of subtle jurisprudential trends within massive datasets of legal texts. AI promises to liberate scholars and jurists to focus their uniquely human capabilities on the higher-order interpretive, ethical, and contextual deliberations that remain indispensable to the enduring vitality of Islamic jurisprudence.

3. Challenges in Structuring Islamic Data Sources

  3.1 Disparities in Language and Data Availability

One prominent challenge is the discrepancy between Arabic and English online content. English-language data sources dominate digital platforms with extensive corpora and advanced tools, whereas Arabic resources remain limited in both volume and accessibility.[5] This imbalance presents significant hurdles for the development of Arabic-based AI applications, particularly in domains requiring nuanced and highly specific legal and historical data, such as Islamic jurisprudence. Additionally, domain-specific data collection and curation pose challenges. Unlike the relatively standardized datasets available for English-language legal studies, Islamic legal data spans a wide range of heterogeneous sources—structured, semi-structured, and unstructured formats—that often lack consistency and interoperability. These limitations hinder both the scalability of AI applications and the integration of traditional Islamic scholarship into modern technology.[6]

For instance, when comparing the digital footprint of legal resources, platforms like Westlaw[7] and LexisNexis,[8] primarily focused on Anglo-American law, offer massive, meticulously curated databases, alongside sophisticated search and analytical tools readily adaptable for AI applications. These platforms represent decades of investment and data accumulation in the English legal domain. This long-term investment is now reaping substantial rewards with the introduction of AI assistants like Westlaw Edge[9] and Lexis+ AI.[10] These tools leverage the meticulously structured datasets to provide context-sensitive, rapid legal research and streamlined analysis, significantly enhancing efficiency and reducing costs involved in legal tasks.

Conversely, while valuable Arabic digital libraries like Al-Maktaba al-Shamela[11] and Al-Maktaba al-Waqfiya[12]  provide crucial access to Islamic texts, they often lack the comprehensive scale, advanced search functionalities, and readily available APIs (Application Programming Interface) for seamless integration with AI tools that are standard in English legal tech. Furthermore, the vast majority of publicly available, large-scale Natural Language Processing (NLP) datasets and pre-trained language models, such as those used in developing cutting-edge AI, are overwhelmingly trained on English text. This disparity means that AI models developed using these resources are inherently better equipped to process and understand English legal materials than their Arabic counterparts, demanding significant additional effort to adapt and fine-tune them for the complexities of the Arabic Islamic legal tradition. This performance gap is not merely a matter of linguistic difference; it is a consequence of the systemic under-representation of Arabic and other non-English languages in the very datasets and algorithmic architectures that underpin contemporary AI.[13]

  3.2 Incomplete and Uncurated Digital Collections

Another major obstacle arises when digitized collections remain uncurated or incomplete. Missing or inconsistent data can create biases and skew interpretations, boosting dominant jurisprudential positions—such as those from certain madhāhib—while pushing minority perspectives to the margins.[14] These imbalances undermine both the credibility of AI-driven jurisprudence and the trust of Muslim communities and legal scholars. By contrast, structured resources—ranging from fatwā databases categorized by madhhab to specialized Islamic finance repositories—offer a more reliable foundation for AI applications, as they facilitate accurate representation and more balanced interpretive outcomes. These resources allow rapid source comparison and pattern recognition but risk rigidity for more nuanced inquiries.

Semi-structured data, as exemplified by annotated primary texts in platforms like SHARIAsource, is organized using metadata fields (e.g., topic, date, author, and legal classification) alongside domain-specific ontologies and taxonomies. This systematic annotation supports fine-tuning of AI models, enhances data analysis, and produces higher-quality training datasets for LLMs. For instance, detailed metadata allows AI to learn nuanced legal concepts and improves semantic search accuracy, resulting in more reliable legal insights than would be possible with non-structured data.

In contrast, unstructured data—such as scanned manuscripts, audiovisual recordings, and social media discussions—lacks standardized annotations, making it challenging for AI/LLMs to extract contextual information and discern critical nuances. While advanced tools such as Optical Character Recognition (OCR) for text conversion, speech-to-text engines for transcription, and NLP techniques are employed to process these materials, their effectiveness is limited by the absence of structured metadata. This unstructured format increases the likelihood of errors, such as misinterpretation of diacritical marks or rare lexical forms, ultimately leading to flawed legal analyses. Thus, the integration of structured and semi-structured data is essential for bridging the performance gap and advancing AI-assisted Islamic jurisprudence.

OCR systems, for instance, frequently struggle with cursive scripts or deteriorated manuscripts, leading to inaccuracies that compromise the reliability of AI outputs.[15] OCR systems, even state-of-the-art models, can still exhibit suboptimal performance when confronted with the specific characteristics of Arabic script, especially highly cursive styles such as Khaṭṭ naskh (figure 1) and Khaṭṭ-uṯ-Ṯuluṯ (figure 2) that are prevalent in many historical Islamic manuscripts. These scripts, often compounded by the degraded condition of older documents—faded ink, damaged parchment, and non-standardized layouts—lead to elevated error rates compared to Latin-script OCR. Studies have documented significant challenges in achieving high accuracy OCR on historical Arabic documents. For example, Kiesling et al., in their case study of open-source Arabic OCR highlight the challenges in processing diacritics and rare lexical forms essential for classical Arabic, with error rates that hinder reliable semantic analysis.[16] Further exacerbating these issues, inconsistencies in digitization practices themselves, such as variations in image resolution, lighting conditions, and pre-processing techniques across different digital collections, introduce further variability and hinder the development of robust, generalizable OCR solutions for Islamic textual heritage.

Meanwhile, speech-to-text algorithms encounter significant obstacles due to the multifaceted nature of Arabic, particularly its regional dialects and specialized terminology, which severely restricts their usability for comprehensive Islamic jurisprudence applications. The very linguistic structure of Arabic presents initial complexities. Unlike more uniform languages, Arabic exhibits a phenomenon known as diglossia, as highlighted by Chemnad and Othman in their review of Arabic speech technologies.[19] This diglossic reality means that within the Arabic-speaking world, Modern Standard Arabic (MSA), the formal, written standard, coexists with a wide array of regional dialects used in everyday spoken communication. These dialects, as Chemnad and Othman point out, are not simply accent variations; they diverge substantially in phonology (pronunciation), lexicon (vocabulary), and even syntactic structures from MSA and amongst themselves.[20] Therefore, expecting speech-to-text systems trained primarily on MSA to accurately transcribe and understand the nuances of diverse Arabic dialects is inherently problematic.

For example, general-purpose solutions such as Google Cloud Speech-to-Text[21] and Microsoft Azure Cognitive Services[22] offer robust multi-language support, utilizing extensive training data that covers many languages including Modern Standard Arabic. However, their performance may suffer when processing regional dialects or specialized legal terminology. In contrast, specialized tools like NeuralSpace[23] and Klaam[24] are designed specifically for Arabic transcription. They employ dialect-specific models and are trained on regionally diverse datasets, which enhances accuracy and ensures nuanced transcription of complex regional Arabic.

Consider legal discussions or fatwā pronouncements in dialects such as Egyptian Arabic or Maghrebi, which significantly diverge in phonetics and vocabulary from MSA. Standard speech-to-text engines, often developed and optimized for MSA due to its prevalence in training corpora like news broadcasts, are likely to encounter difficulties with the acoustic and linguistic features of these dialects. This gap presents a significant challenge for digitizing and processing spoken Islamic legal content effectively.

3.3 Challenges in Digitizing and Analyzing Classical Arabic Texts

Digitization errors can erase or distort nuances in classical Arabic syntax. The dense morphology and complex semantics present significant challenges for NLP tools not designed to handle diacritical marks or rare lexical forms. To address these complexities, AI models must be designed to understand the meaning (semantic parsing) and structure (morphological analysis) of classical Arabic texts. For example, the Arabic word “كِتَاب” (kitāb), while commonly understood as “book,” in Islamic legal contexts, especially within manuscript traditions, the nuanced presence or absence of diacritics can radically alter its jurisprudential implications. Depending on the diacritization—or crucially, mis-diacritization by OCR—the word could potentially be misread as a variant with a distinct legal meaning, possibly referring to specific types of legal documents, contractual agreements, or categories of jurisprudential works (kutub). Consider a scenario in which an OCR error subtly alters “كِتَاب,” causing an AI tool to misidentify a manuscript passage. This error may lead to incorrect text categorization, faulty retrieval of related rulings, and ultimately, flawed legal analysis based on a compromised digital source.

This is not simply a theoretical concern. Research in digital humanities and Arabic NLP has underscored the vulnerability of classical Arabic to OCR errors, which can significantly impact the accuracy of downstream tasks. For instance, Alghyaline highlights that the morphological richness and complex cursive nature of Arabic script are key factors contributing to the high error rates of OCR systems, making the preservation of nuances in classical Arabic texts an especially difficult undertaking for NLP tools.[25] Furthermore, the study emphasizes that “the OCR for Arabic script is still an unsolved problem for printed and handwritten scripts, especially for page-level scripts.”[26]

Even with these hurdles, progress is evident. Initiatives like the Fatwāset[27] dataset offer promising solutions by aggregating fatwās with metadata, enabling tasks such as question answering and topic classification.[28] Projects like the Open Islamicate Texts Initiative (OpenITI) and the Qatar Digital Library are paving the way for more robust corpus creation and annotation. OpenITI, for example, focuses on creating machine-readable, standards-compliant corpora of Islamicate texts, allowing for advanced computational analysis.[29] The Qatar Digital Library provides extensive collections of historical texts and multimedia, contextualized with metadata for deeper insights.[30] These initiatives are vital for digitizing and annotating extensive collections of Islamic texts, enabling researchers and AI tools to better track juristic trends across madhāhib and regions. However, unbalanced data representation remains a significant risk. Datasets disproportionately representing specific geographical or doctrinal perspectives can mislead fatwā issuance or comparative legal analyses. A diverse, representative dataset is essential to maintain the epistemic integrity of Islamic legal studies.

On another hand, researchers are continually developing models tailored to Islamic heritage data. A recently developed architecture, Qalam, appears to be capable of transcribing Arabic written materials by employing a SwinV2 encoder and a RoBERTa decoder. Simply put, Qalam converts handwritten Arabic texts into digital form with very few errors, making it much more effective than older transcription methods. Preliminary evaluations suggest that it may outperform previously used models, although further testing is needed to confirm its capabilities. Notable advances include improved handling of challenging Arabic diacritics and higher-resolution image processing than traditional OCR tools. Indeed, the result from using this new model is impressive: researchers are obtaining an incredible 0.80% Word Error Rate in the domain of handwriting recognition, while 1.18% in normal OCR transcription tasks from real world complex Arabic datasets, showing impressive capabilities for current cutting edge Arabic character transcription methods, with good precision that could be highly applicable.[31]

We are also gaining a better grasp on local variations. We now see dialect-specific language models that actually can pick up regional characteristics which were previously challenging for speech-to-text systems. Models such as SaudiBERT[32] and AlcLaM[33] work by learning regional features by using data from specific domains. AlcLaM, for example, which was trained on 3.4 million sentences obtained from real world Arabic social media exchanges, has shown promising performance, outperforming even larger and more complex NLP models.[34] This capability shows that the approach taken in this type of learning could enhance efficiency and facilitate adaptation to various contexts, including extracting contextual insights from audio content.

NLP has become a crucial element in structuring this evolving data ecosystem. Basic techniques such as tokenization and stemming, while foundational, are insufficient for capturing the depth and nuances inherent in classical Arabic texts; they analyze texts only at a basic level.[35] More advanced transformer architectures have demonstrated considerable promise, yet their efficacy increasingly benefits from diverse, high-quality corpora that combine structured, semi-structured, and unstructured data, rather than relying solely on comprehensive manual annotation. Self-supervised learning techniques, weak supervision, and unsupervised pretraining methods can significantly mitigate the need for extensive human-labeled data while achieving state-of-the-art performance across various NLP tasks.[36] However, challenges such as domain adaptation and bias mitigation still necessitate careful corpus curation to ensure representation fairness and contextual accuracy, particularly in specialized domains like Islamic jurisprudence. One key innovation lies in hybrid methods that integrate structured data for canonical references, semi-structured datasets for contextual insights, and unstructured content for broader interpretive depth.[37] This hybrid approach reflects the multi-layered nature of Islamic law, where foundational texts (uṣūl al-fiqh) provide the core legal framework, while judicial interpretations, scholarly debates, and community practices dynamically supplement and enrich that framework, producing a living legal tradition.

3.4 Safeguarding Ethical and Methodological Imperatives in AI-Assisted Jurisprudence

Ethical and jurisprudential considerations lie at the heart of AI applications in Islamic law.^[38] AI systems must uphold methodological rigor and epistemic integrity, ensuring that their outputs align with established principles of Islamic law. The law is built on strict methodological principles (uṣūl al-fiqh) that require jurists to derive rulings from textual and non-textual sources through structured reasoning.[39] AI systems designed to assist in issuing fatwās or interpreting legal texts must replicate this rigorous process, following established principles of tafsīr (exegesis), source verification and overall guidance from uṣūl al-fiqh. Yet many current algorithmic models prioritize speed and efficiency over deep contextual analysis,[40] risking an oversimplification of the deeper ethical objectives (maqāṣid al-sharīʿa) inherent in Islamic law. Maqāṣid al-sharīʿa—referring to the higher objectives such as the preservation of life, intellect, faith, lineage, and property—ensure that legal rulings promote overall justice and social welfare.[41]

A critical risk is AI “hallucination”—generating false information,[42] such as fabricated ḥadīths or misattributed scholarly opinions. These hallucinations occur frequently. For example, AI models may generate references to non-existent books or articles and erroneously attribute ideas to established scholars even when no such works exist. Such fabrications risk distorting academic discourse and can rapidly proliferate misleading information across digital platforms. Moreover, these fabricated outputs can be particularly difficult to detect for individuals without advanced training in Islamic sources and methodologies. Their rapid spread can further distort public understanding and erode communal trust in traditional jurisprudence. Algorithmic bias further compounds these issues. Algorithmic bias is another concern. As O’Neil highlights, AI systems are only as objective as the data they are trained on,[43] and imbalanced datasets may reinforce the dominance of major madhhabs (e.g., Ḥanafī or Shāfiʿī) while marginalizing minority perspectives. This bias can distort comparative analyses (muqāranat al-madhāhib) and undermine the pluralistic ethos of Islamic jurisprudence, where diverse legal opinions are essential.

Communal trust in AI-generated religious content is essential. When AI systems fail to adhere to established uṣūl and methodologies, or when they hallucinate and exhibit algorithmic bias, the credibility of both traditional and AI-assisted scholarly outputs is compromised. Tsuria and Tsuria demonstrate that when AI’s representation of religious narratives is detached from rigorous scholarly frameworks, it can distort public understanding and undermine the authority of Islamic scholarship.[44] Similarly, Patel et al. discuss the technical challenges involved in developing domain-specific large language models that faithfully represent the Islamic worldview, emphasizing that high-quality, culturally sensitive datasets are critical for preserving the integrity of Islamic text generation.[45]

4. The Future of AI-Assisted Islamic Jurisprudence

Creating a unified digital corpus for Islamic texts requires more than mere digitization. It demands thoughtful curation, rigorous metadata tagging, and attention to diverse juristic viewpoints. Such efforts may help lay the groundwork for training advanced NLP models capable of engaging with both classical and modern Arabic sources. When data is comprehensive and balanced, AI systems become more reliable tools for in analyzing and generating jurisprudential insights. Thus, collaboration between Islamic scholars, data scientists, and NLP/ LLMs specialists is not merely beneficial but crucial. Islamic scholars provide the contextual understanding needed to define taxonomies, guide annotation processes, and validate AI outputs. Data scientists and NLP engineers, similarly, bring the technical expertise necessary to develop and deploy advanced models capable of processing large, complex datasets. This partnership ensures that AI applications in Islamic law are both technically robust and jurisprudentially sound. Collaborative workshops, shared research agendas, and joint publications can further strengthen these interdisciplinary efforts, fostering a unified approach to data structuring and analysis.

Several ongoing projects exemplify the integration of AI and digital tools in Islamic legal studies. For instance, Harvard Law School’s Program in Islamic Law leads the SHARIAsource project, offering a comprehensive portal that aggregates digitized Islamic legal texts, data tools, and expert analyses.[46] A notable tool developed within SHARIAsource is CorpusBuilder, designed to facilitate the digitization of Arabic-script texts to enhance their accessibility for contemporary research.[47] CorpusBuilder is now integrated into eScriptorium, a platform that brings together multiple projects such as OpenITI and the Arabic-script OCR Catalyst Project (AOCP) to facilitate and streamline digital manuscript analysis for researchers working with primary source material. While SHARIAsource has taken an important first step towards creating an infrastructure for AI research on legal data, through the incorporation of Islamic primary sources, a new project, known as Usul.ai is taking these steps even further.

Usul.ai, a new initiative in this field, focuses on training advanced AI models on foundational Islamic legal texts. The project, which aims to incorporate over 8,000 texts in its digital library, emphasizes improving search capabilities within a large corpus of Islamic legal materials.^[48] Unlike earlier projects that focused mainly on digitization, Usul.ai incorporates advanced AI technologies, including large language models similar to OpenAI’s GPT-4o, to facilitate more efficient and nuanced text analysis. This project seeks to address a frequent problem that hinders much traditional scholarship: materials often not available except in PDF format. This forces researchers to often resort to other search methods beyond the reach of an online platform for sources therefore slowing down scholarship and limiting availability.

Similarly, NYU Abu Dhabi’s CAMeL Lab (Computational Approaches to Modeling Language) contributes to the broader ecosystem by building essential tools for Arabic language processing,[49] such as the CALIMA-STAR morphological analyzer and the MADAR Corpus—a large digital collection of Arabic texts. CAMeL Lab provides the underlying computational technology—including syntactic parsers and systems for standardizing diverse Arabic scripts—that enables the processing of traditional documents such as legal manuscripts and Qur’ānic commentaries.[50]

Furthermore, Open Islamicate Texts Initiative (OpenITI) provides a compelling case in point. OpenITI is a collaborative, interdisciplinary effort to create a machine-readable, standards-compliant corpus of premodern Islamicate texts, including classical Arabic manuscripts.[51] While it represents a significant step forward in digitizing Islamic legal sources, it also illustrates several of the challenges discussed. For instance, OpenITI’s corpus, which currently includes over 7,000 texts, does not yet encompass the entirety of relevant Arabic manuscripts, highlighting the fragmented nature of Islamic data sources.[52] Additionally, similar to SHARIAsource, OpenITI relies on OCR technology, which, despite its sophistication, struggles with cursive scripts and degraded manuscripts.[53]  Nonetheless, notable progress has been achieved through initiatives like the Arabic-script OCR Catalyst Project (AOCP), which has achieved character accuracy rates (CARs) of ≥97% for common Arabic and Persian typefaces.[54] These OCR inaccuracies can distort critical nuances in classical Arabic syntax, such as diacritical marks or rare lexical forms, which are essential for accurate jurisprudential analysis

The trajectory of AI in Islamic jurisprudence is increasingly shaped by sophisticated methodologies that integrate linguistic, cultural, and contextual dimensions. Moving beyond textual corpora alone, future AI applications could integrate audio-visual records of scholarly discourse, social media debates reflecting contemporary legal concerns, and real-world communal practices, allowing AI systems to approximate the complex and multi-layered nature of legal deliberation across regions. This shift reflects a deeper ambition: to mirror the nuanced realities in which sharīʿa principles are applied, recognizing that legal rulings often draw upon localized customs and communal norms.

Teams of jurists, linguists, and technologists must work together to develop robust corpora that integrate classic texts with contemporary discussions, ensuring that both heritage sources and modern insights are adequately represented. In parallel, more “context-aware” NLP models will incorporate domain-specific knowledge about madhāhib, methodologies (uṣūl al-fiqh), and ethical frameworks, thus offering search results and analytical inferences attuned to the subtleties of Islamic jurisprudential reasoning.

Thoughtfully deployed, AI in Islamic jurisprudence offers a compelling augmentation, not a mere substitution, for traditional scholarly endeavors. Its utility extends beyond expedited text retrieval and summary, encompassing capabilities critical to advanced legal reasoning, nuanced variant identification across differing madhāhib, and large-scale doctrinal analysis previously unimaginable. AI tools, when meticulously crafted and rigorously validated, promise to liberate scholars from time-consuming manual tasks, allowing for a concentration of intellectual labor on higher-order interpretive and ethical deliberations. However, these tangible benefits are contingent upon a commitment to methodological integrity that matches, if not surpasses, established jurisprudential standards.  The integration of AI demands a considered recalibration: while embracing technological advancements, the enduring values of reasoned discourse, contextual sensitivity, and profound ethical reflection—cornerstones of Islamic legal tradition—must not merely be preserved, but amplified in the face of emerging computational methodologies, especially those promising rapid but shallow analysis that can undermine long term value of religious scholarship.

5. Conclusion

AI holds tremendous promise for transforming Islamic jurisprudence. It can speed up legal interpretation, enhance comparative analysis, and uncover patterns in vast textual sources. Yet, this potential faces significant challenges: a notable imbalance in Arabic versus English data, incomplete and unevenly curated digital collections, difficulties processing unstructured Arabic data sources, and the need for AI systems to meet the high ethical and methodological standards of traditional fiqh.

This essay’s discussion has drawn on evidence from initiatives such as SHARIAsource, Usul.ai, OpenITI, and NYU Abu Dhabi’s CAMeL Lab. These projects illustrate viable pathways to address current limitations and guide us toward a future of augmented scholarly practice. Moving forward, effective collaboration between Islamic scholars, data scientists, and NLP specialists is essential. This interdisciplinary collaboration is crucial for building robust, context-aware AI tools that respect the nuanced legacy of Islamic legal tradition. By prioritizing rigorous data, ethical grounding, and interdisciplinary partnership, we can ensure AI serves to enrich Islamic jurisprudence, amplifying its enduring legacy for the future.

Glossary

• Artificial Intelligence (AI): Computer systems that perform tasks requiring human-like intelligence.
• Application Programming Interface (API): A set of protocols and tools that enable software applications to communicate with each other.
• Large Language Models (LLMs): AI models trained on massive text data to generate and analyze language.
• Natural Language Processing (NLP): Techniques that enable computers to understand and produce human language.
• Optical Character Recognition (OCR): Technology that converts scanned images of text into editable, machine-readable text.
• Retrieval-Augmented Generation (RAG): A method that combines document retrieval with AI text generation.
• Digital Corpus: An organized collection of digital texts used for analysis and training.
• Structured Data: Data organized in a clear format, such as in databases or spreadsheets.
• Semi-Structured Data: Data with some organization (e.g., XML or JSON), but not fully tabular.
• Unstructured Data: Data without a predefined format, like free text or images.
• Tokenization: Splitting text into words or phrases for analysis.
• Stemming: Reducing words to their base form.
• Diacritical Marks: Small symbols that indicate pronunciation, crucial in languages like Arabic.
• Morphological Analysis: Examining the structure of words to understand their meaning.
• Syntactic Parsing: Analyzing sentence structure to determine grammatical relationships.
• Transformer Architecture: A neural network design that underpins many modern NLP models.
• Self-Supervised Learning: Training models using raw data without explicit labels.
• Domain Adaptation: Adjusting a model to perform well on data from a different domain.
• Bias Mitigation: Methods to reduce unfair biases in AI systems.
• Word Error Rate (WER): A measure of transcription errors in recognized text.
• Character Accuracy Rate (CAR): The percentage of characters correctly recognized by an OCR system.
• Hybrid Methods: Combining different data types or techniques to improve model performance.
• Speech-to-Text Algorithms: Systems that convert spoken language into written text.
• Dialect-Specific Language Models: NLP models tuned to recognize and process specific regional dialects.

Notes:

[1] Omar Essam, “Building a Basic Fatwā Chat Bot,” January 20, 2018, accessed December 18, 2024, https://omarito.me/building-a-basic-fatwā-chat-bot/.

[2] “AI Fatwā Assistance Platform,” Mufti.ai, accessed January 20, 2025, https://www.mufti.ai.

[3] “About Usul,” Usul, accessed January 31, 2025, https://www.usul.ai/about.

[4] “Computational Approaches to Modeling Language Lab,” New York University Abu Dhabi, accessed February 2, 2025, https://nyuad.nyu.edu/en/research/faculty-labs-and-projects/computational-approaches-to-modeling-language-lab.html.

[5] See Imane Guellil et al., “Natural Language Processing for Arabic Legal Texts: Challenges and Innovations,” Digital Scholarship in Islamic Studies 12, no. 2 (2021): 89–110.

[6] Muhammad Huzaifa Bashir et al., “Arabic natural language processing for Qur’anic research: a systematic review,” Artificial Intelligence Review 56, no. 7 (2023): 6801–54.

[7]  “Legal Research Platforms,” Westlaw, accessed January 26, 2025, https://legal.thomsonreuters.com/en/westlaw.

[8] “About Us,” LexisNexis, accessed January 26, 2025, https://risk.lexisnexis.com/about-us.

[9] “Westlaw Edge,”Thomson Reuters, accessed February 12, 2025, https://legal.thomsonreuters.com/en/westlaw.

[10] “Lexis+ AI,” LexisNexis, accessed February 12, 2025, https://www.lexisnexis.com.

[11] Al-Maktaba al-Shamela, “التعريف بالمكتبة,” accessed January 26, 2025, https://shamela.ws.

[12] Al-Maktaba al-Waqfiya, “التعريف بالمكتبة,” accessed January 26, 2025, https://waqfeya.net.

[13] Roberto Navigli, Simone Conia and Björn Ross, “Biases in large language models: origins, inventory, and discussion,” ACM Journal of Data and Information Quality 15, no. 2 (2023): 6.

[14] Sofia Tsourlaki, “Artificial Intelligence on Sunni Islam’s Fatwa Issuance in Dubai and Egypt,” Islamic Inquiries 1, no. 2 (2022): 107–25.

[15] Safiullah Faizullah et al., “A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges,” Applied Sciences 13, no. 7 (2023): 4584.

[16] Benjamin Kiessling, Gennady Kurin, Matthew Thomas Miller and Kader Smail, “Advances and Limitations in Open Source Arabic-Script OCR: A Case Study,” arXiv preprint arXiv:2402.10943 (2024): 18–20.

[17] “Qurʾan in Naskh Style,” Wikimedia Commons, accessed February 13, 2025, https://commons.wikimedia.org/w/index.php?search=Qur%27an+Naskh+Style&title=Special:MediaSearch&type=image.

[18] “The Alphabet in Thuluth Script by ‘Ala’ al-Din Tabrizi,” Wikimedia Commons, accessed February 13, 2025, https://commons.wikimedia.org/wiki/File:The_alphabet_in_Thuluth_script_by_%27Ala%27_al-Din_Tabrizi.jpg.

[19] Khansa Chemnadand Achraf Othman, “Advancements in Arabic Text-to-speech systems: a 22-year literature review,” IEEE Access 11 (2023): 30929–54.

[20] Ibid., 30930.

[21] “Google Cloud Speech-to-Text,” Google, accessed February 14, 2025, https://cloud.google.com/speech-to-text .

[22] “Microsoft Azure Cognitive Services: Speech to Text,” Microsoft, accessed February 14, 2025, https://azure.microsoft.com/en-us/services/cognitive-services/speech-to-text/.

[23] “NeuralSpace,” NeuralSpace, accessed February 14, 2025, https://neuralspace.ai.

[24] “Klaam,” GitHub Repository, accessed February 14, 2025 https://github.com/klam-repo.

[25] Salah Alghyaline, “Arabic Optical Character Recognition: A Review,” CMES-Computer Modeling in Engineering & Sciences 135, no. 3 (2023): 1826–27.

[26] Ibid.

[27] “Ohoud. Fatwaset,” GitHub repository, accessed February 14, 2025, https://github.com/ohoud/Fatwaset.

[28] Ohoud Alyemny, Hend Al-Khalifa and Abdulrahman Mirza, “A Data-Driven Exploration of a New Islamic Fatwas Dataset for Arabic NLP Tasks,” Data 8, no. 10 (2023): 155.

[29] “Digitizing and Processing Classical Arabic Texts,” Open Islamicate Texts Initiative, accessed December 19, 2024, https://openiti.org/.

[30] “Digitizing and Preserving Islamic Heritage,” Qatar Digital Library, accessed December 19, 2024, https://www.qdl.qa/.

[31] Gagan Bhatia, El Moatez Billah Nagoudi, Fakhraddin Alwajih and Muhammad Abdul-Mageed, “Qalam: A Multimodal LLM for Arabic Optical Character and Handwriting Recognition,” arXiv preprint arXiv:2407.13559 (2024).

[32] Faisal Qarah, “SaudiBERT: A Large Language Model Pretrained on Saudi Dialect Corpora,” arXiv preprint arXiv:2405.06239 (2024).

[33] Murtadha Ahmed, Saghir Alfasly, Bo Wen, Jamaal Qasem, Mohammed Ahmed, and Yunfeng Liu, “AlcLaM: Arabic Dialectal Language Model,” arXiv preprint arXiv:2407.13097 (2024).

[34] Ibid.

[35] Guellil et al., “Natural Language Processing,” 89–110.

[36] Yanchao Sun et al., “Smart: Self-supervised multi-task pretraining with control transformers,” arXiv preprint arXiv:2301.09816 (2023).

[37] Ahmed Ramzy et al., “Hadiths Classification Using a Novel Author-Based Hadith Classification Dataset (ABCD),” Big Data and Cognitive Computing 7, no. 3 (2023): 141.

[38] Ezieddin Elmahjub, “Artificial Intelligence (AI) in Islamic Ethics: Towards Pluralist Ethical Benchmarking for AI,” Philosophy & Technology 36, no. 4 (2023): 73.

[39] Mohammad Hashim Kamali, Principles of Islamic Jurisprudence (3rd ed. Cambridge: Islamic Texts Society, 2003).

[40] Emily M. Bender, Timnit Gebru, Angelina McMillan-Major and Shmargaret Shmitchell, “On the dangers of stochastic parrots: Can language models be too big?,” in Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (Association for Computing Machinery, 2021), 610–23.

[41] Muhammad al-Tahir ibn ‘Ashur, Treatise on Maqāṣid al-Sharīʿa, trans. Mohamed El-Mesawi (The International Institute of Islamic Thought, 2006).

[42] Lei Huang et al., “A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions,” arXiv:2311.05232 (2024).

[43] Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (Crown, 2017).

[44] Ruth Tsuria and Yossi Tsuria, “Artificial Intelligence’s Understanding of Religion: Investigating the Moralistic Approaches Presented by Generative Artificial Intelligence Tools,” Religions 15, no. 3 (2024): 375.

[45] Shabaz Patel, Hassan Kane and Rayhan Patel. “Building Domain-Specific LLMs Faithful To The Islamic Worldview: Mirage or Technical Possibility?,” arXiv preprint arXiv:2312.06652 (2023).

[46] “SHARIAsource Portal,” Program in Islamic Law at Harvard Law School, accessed January 13, 2025, https://portal.shariasource.com/.

[47] “CorpusBuilder,” Open Islamicate Texts Initiative, last modified December 3, 2024, https://openiti.org/projects/CorpusBuilder.html.

[48] “About Usul,” Usul, accessed January 31, 2025, https://www.usul.ai/about.

[49] See note 4 above.

[50] Ibid.

[51] “Open Islamicate Texts Initiative,” OpenITI Corpus, accessed January 13, 2025, https://openiti.org/projects/OpenITI%20Corpus.html.

[52] “GitHub,” OpenITI/RELEASE, accessed January 13, 2025, https://github.com/OpenITI/RELEASE.

[53] “OpenITI, OCR, and Textual Criticism,” KITAB Project (blog), July 16, 2020, https://kitab-project.org/OpenITI,-OCR,-and-Textual-Criticism/.

[54] “OpenITI AOCP Phase Two,” Open Islamicate Texts Initiative, accessed January 13, 2025, https://openiti.org/projects/OpenITI%20AOCP%20Phase%20Two.html.

(Suggested Bluebook citation: Ezieddin Elmahjub, From Manuscripts to Digital Corpus: Structuring Islamic Data Sources for the Future of AI Jurisprudence, Islamic Law Blog (Mar. 27, 2025), https://islamiclaw.blog/2025/03/27/roundtable-from-manuscripts-to-digital-corpus-structuring-islamic-data-sources-for-the-future-of-ai-jurisprudence/)

(Suggested Chicago citation: Intisar Rabb, “From Manuscripts to Digital Corpus: Structuring Islamic Data Sources for the Future of AI Jurisprudence,” Islamic Law Blog, March 27, 2025, https://islamiclaw.blog/2025/03/27/roundtable-from-manuscripts-to-digital-corpus-structuring-islamic-data-sources-for-the-future-of-ai-jurisprudence/)