Prediction of Turkish Constitutional Court Decisions with Explainable Artificial Intelligence

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 (K-Nearest Neighbors Algorithm), SVM (Support Vector Machine), DT (Decision Tree), RF (Random Forest), and XGBoost (Extreme Gradient Boosting) machine learning (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 (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.