TY  - JOUR
T1  - Binary Logic as Legal Algorithm: Computational Evidence of Systematic Decision-Making in Ottoman Sheikh al-Islam Fatwas
TT  - Yasal Bir Algoritma Olarak İkili Mantık: Osmanlı Şeyhulislam Fetvalarında Sistematik Karar Vermenin Hesaplanabilir Kanıtları
AU  - Çetinkaya, Ali
PY  - 2025
DA  - September
Y2  - 2025
DO  - 10.5281/zenodo.17337470
JF  - DiHA: Journal of Interdisciplinary Legal Studies
JO  - DiHA
PB  - AREA STUDIES ASSOCIATION
WT  - DergiPark
SN  - 3062-0473
SP  - 42
EP  - 70
IS  - 2
LA  - en
AB  - Bridging computational linguistics, machine learning, and Islamic legal studies, this interdisciplinary investigation examines the algorithmic structure underlying Ottoman Sheikh al-Islam fatwa reasoning. Analysis of 9,913 fatwas from authoritative collections—including Fetâvâ-yı Feyziye, Fetâvâ-yı Ebüssuûd Efendi, and Behcetü'l-Fetâvâ—establishesthat nearly 87% of rulings follow binary prohibition/permission logic. This binary architecture exhibits notably low entropy (1.599 bits), falling 66% below maximum possible entropy, which suggests highly structured decision patterns characteristic of thecentralized Sheikh al-Islam authority. Machine learning models achieved considerable predictive accuracy (87.6% with XGBoost), with simple linguistic markers ("olmaz"/"olur") emerging as the strongest predictors. Among binary cases, prohibitions (4,410) and permissions (4,199) appear balanced, challenging assumptions about Islamic law's supposedly restrictive nature. Financial matters pointed toeven higher binary classification rates than non-financial cases (88% versus 84%), likely reflecting commercial law's demand for clear guidance. Such systematic patterns, developed centuries before digital computing, suggest that algorithmic legal reasoning may represent an ancient practice rather than a modern innovation. These resultscarry implications for contemporary debates around legal automation and the appropriate role of Artificial Intelligence(AI)in religious jurisprudence, while documentinghow pre-modern institutions achieved systematization through human rather than technological means.
KW  - Sheikh  al-Islam
KW  - Ottoman  Fatwas
KW  - Computational  Jurisprudence
KW  - Islamic Law
KW  - Legal Algorithms
KW  - Binary Logic
KW  - Machine Learning
N2  - Bu disiplinlerarası araştırma, Osmanlı şeyhülislamlarının fetva mantığının temelindeki algoritmik yapıyı açığa çıkarmak amacıyla hesaplamalı dilbilim, makine öğrenmesi, İslam hukuku ve bilgi teorisi arasında bir köprü kurmaktadır. Fetâvâ-yı Feyziye, Fetâvâ-yı Ebüssuûd Efendi ve Behcetü’l-Fetâvâ gibi otoriter kaynaklardan seçilen 9.913 fetvanın incelenmesi sonucunda, İslam hukukunun özünde ikili bir algoritmik düzen üzerine işlediği ve kararların %86,8’inin “yasaklama/izin” mantığına dayandığı ortaya konmuştur. Sistem, olası en yüksek entropi düzeyi olan 4.755 bitten %66,4 oranında daha düşük bir seviyeyi temsil eden 1.599 bitlik dikkat çekici bir düşük entropi sergilemektedir. Bu durum, Osmanlı’daki şeyhülislamlık kurumunun merkezinde gelişen hukuk hiyerarşisinin, oldukça yapılandırılmış ve öngörülebilir karar kalıpları ürettiğini göstermektedir. Makine öğrenmesi modelleri, fetvaları tahmin etmede %87,6 doğruluk oranına (XGBoost) ulaşmakta; özellikle “olur/olmaz” gibi ikili dilsel işaretler en güçlü belirleyici öğeler olarak öne çıkmaktadır. Yasaklama-izin ayrımı, 4.410 yasaklama ve4.199 izin kararıyla dengeli bir içtihat dağılımı göstermektedir. Ayrıca, finansal davalarda ikili sınıflandırma oranı %88,3 iken, finansal olmayanlarda bu oran %84,3’tür. Bu fark, ekonomik işlemlerin daha net hukuki açıklığa ihtiyaç duyduğunu işaret etmektedir. Sonuç olarak bu çalışma, Osmanlı şeyhülislam sistemi üzerinden kurumsallaşan İslam içtihadının, modern bilişimden yüzyıllar önce algoritma benzeri karar yapıları geliştirdiğine dair hesaplanabilir kanıtlar sunmaktadır. Bulgular aynı zamanda günümüz yasal otomasyonu ve dini bağlamlarda yapay zekâ etiği üzerine de önemli çıkarımlar barındırmaktadır.
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UR  - https://doi.org/10.5281/zenodo.17337470
L1  - https://dergipark.org.tr/en/download/article-file/6125704
ER  -