Prediction of Turkish Constitutional Court Decisions with Explainable Artificial Intelligence

Authors : Tülay TURAN, Ecir KÜÇÜKSİLLE, Nazan KEMALOĞLU ALAGÖZ

Pages : 128-141

Doi:10.30516/bilgesci.1317525

View : 46 | Download : 27

Publication Date : 2023-09-30

Article Type : Research Paper

Abstract :Using artificial intelligence in law is a topic that has attracted attention in recent years. This study aims to classify the case decisions taken by the Constitutional Court of the Republic of Turkey. For this purpose, open-access data published by the Constitutional Court of the Republic of Turkey on the website of the Decisions Information Bank were used in this research. KNN insert ignore into journalissuearticles values(K-Nearest Neighbors Algorithm);, SVM insert ignore into journalissuearticles values(Support Vector Machine);, DT insert ignore into journalissuearticles values(Decision Tree);, RF insert ignore into journalissuearticles values(Random Forest);, and XGBoost insert ignore into journalissuearticles values(Extreme Gradient Boosting); machine learning insert ignore into journalissuearticles values(ML); algorithms are used. Precision, Recall, F1-Score, and Accuracy metrics were used to compare the results of these models. As a result of the evaluation showed that the XGBoost model gave the best results with 93.84% Accuracy, 93% Precision, 93% Recall, and 93% F1-Score. It is important that the model result is not only good but also transparent and interpretable. Therefore, in this article, using the SHAP insert ignore into journalissuearticles values(SHapley Additive exPlanations); method, one of the explainable artificial intelligence techniques, the features that affect the classification of case results are explained. The study is the first study carried out in our country to use explainable artificial intelligence techniques in predicting court decisions in the Republic of Turkey with artificial intelligence.
Keywords : Explainable Artificial Intelligence, Turkish Constitutional Court, Legal Jugment Prediction, SHAP, XGBoost