Research Article
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Year 2023, Volume: 7 Issue: 2, 128 - 141, 30.09.2023
https://doi.org/10.30516/bilgesci.1317525

Abstract

References

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Prediction of Turkish Constitutional Court Decisions with Explainable Artificial Intelligence

Year 2023, Volume: 7 Issue: 2, 128 - 141, 30.09.2023
https://doi.org/10.30516/bilgesci.1317525

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.

References

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  • Altreas, N., Tsarapatsanis, D., Preoţiuc-Pietro, D., & Lampos, V. (2016). Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective. PeerJ Comput Sci.
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  • Brereton, RG., Lloyd, GR. (2010). Support vector machines for classification and regression. Analyst, 135(2), 230-267.
  • Chalkidis, I., Androutsopoulos, I., Aletras, N. (2019). Neural legal judgment prediction in English. arXiv preprint arXiv:1906.02059.
  • Chen, L., Chen, P., Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278.
  • Chen, T., Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 785-794, Association for Computing Machinery, New York, United States.
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  • Collenette, J., Atkinson, K., Bench-Capon, T. J. (2020). An Explainable Approach to Deducing Outcomes in European Court of Human Rights Cases Using ADFs, In: COMMA, ed. Prakken, H., Bistarelli, S. and Santini, F., 21-32, IOS Press.
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  • Ghorbani, A., Wexler, J., Zou, J. Y., Kim, B. (2019). Towards automatic concept-based explanations. Advances in Neural Information Processing Systems, 32.
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  • Górski, Ł., Ramakrishna, S. (2021, June). Explainable artificial intelligence, lawyer's perspective. In: Proceedings of the Eighteenth International Conference on Artificial Intelligence and Law (pp. 60-68).
  • Gorski, L., Ramakrishna, S., Nowosielski, J. M. (2020). Towards grad-cam based explainability in a legal text processing pipeline. arXiv preprint arXiv:2012.09603.
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  • Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and vascular neurology, 2(4).
  • Jiang, H., He, Z., Ye, G., Zhang, H. (2020). Network intrusion detection based on PSO-XGBoost model. IEEE Access, 8, 58392-58401.
  • Katz, D. M., Bommarito, M. J., Blackman, J. (2017). A general approach for predicting the behavior of the Supreme Court of the United States. PloS one, 12(4).
  • Kaur, A., Bozic, B. (2019). Convolutional Neural Network-based Automatic Prediction of Judgments of the European Court of Human Rights. In: AICS, pp 458-469
  • Kenny, E. M., Ford, C., Quinn, M., Keane, M. T. (2021). Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies. Artificial Intelligence, 294, 103459.
  • Knox, J. (2020). Artificial intelligence and education in China. Learning, Media and Technology, 45(3), 298-311.
  • Labin, S., Segal, U. (2021). AI-driven contract review: A product development journey. In Research Handbook on Big Data Law, 454-466, Edward Elgar Publishing.
  • Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., Müller, K. R. (2019). Unmasking Clever Hans predictors and assessing what machines really learn. Nature communications, 10(1), 1096.
  • Letham, B., Rudin, C., McCormick, T. H., Madigan, D. (2012). Building interpretable classifiers with rules using Bayesian analysis. Department of Statistics Technical Report tr609, University of Washington, 9(3), 1350-1371.
  • Li, S., Zhang, H., Ye, L., Guo, X., Fang, B. (2019). Mann: A multichannel attentive neural network for legal judgment prediction. IEEE Access, 7, 151144-151155.
  • Lin, Y. S., Lee, W. C., Celik, Z. B. (2021). What do you see? Evaluation of explainable artificial intelligence (XAI) interpretability through neural backdoors. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining (pp. 1027-1035).
  • Long, S., Tu, C., Liu, Z., Sun, M. (2019). Automatic judgment prediction via legal reading comprehension. In: Chinese Computational Linguistics: 18th China National Conference, 558-572, Springer International Publishing.
  • Loureiro, S. M. C., Guerreiro, J., Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of business research, 129, 911-926.
  • Lundberg, SM., Lee, SI. (2017). A unified approach to interpreting model predictions. In: Proceedings of the 31st international conference on neural information processing systems, 30, 4768-4777.
  • Ma, L., Zhang, Y., Wang, T., Liu, X., Ye, W., Sun, C., Zhang, S. (2021). Legal judgment prediction with multi-stage case representation learning in the real court setting. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 993-1002).
  • Mangalathu, S., Hwang, S. H., Jeon, J. S. (2020). Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach. Engineering Structures, 219, 110927.
  • Mantovani, R. G., Horváth, T., Cerri, R., Vanschoren, J., De Carvalho, A. C. (2016, October). Hyper-parameter tuning of a decision tree induction algorithm. In: 2016 5th Brazilian Conference on Intelligent Systems (BRACIS) (pp. 37-42). IEEE.
  • Meço, G., Çoştu, F. (2022). Eğitimde Yapay Zekânın Kullanılması: Betimsel İçerik Analizi Çalışması. Karadeniz Teknik Üniversitesi Sosyal Bilimler Enstitüsü Sosyal Bilimler Dergisi, 12(23), 171-193.
  • Mumcuoğlu, E., Öztürk, C. E., Ozaktas, H. M., Koç, A. (2021). Natural language processing in law: Prediction of outcomes in the higher courts of Turkey. Information Processing & Management, 58(5), 102684.
  • Mumford, J., Atkinson, K., Bench-Capon, T. (2021). Machine learning and legal argument. In: CEUR Workshop Proceedings (Vol. 2937, pp. 47-56).
  • Nanfack, G., Temple, P., Frénay, B. (2022). Constraint Enforcement on Decision Trees: A Survey. ACM Computing Surveys (CSUR), 54(10s), 1-36.
  • Nie, W., Zhang, Y., Patel, A. (2018). A theoretical explanation for perplexing behaviors of backpropagation-based visualizations. In: International Conference on Machine Learning, PMLR, 3809-3818.
  • Nikam, SS. (2015). A comparative study of classification techniques in data mining algorithms. Oriental Journal of Computer Science and Technology, 8(1), 13-19.
  • Niklaus, J., Chalkidis, I., Stürmer, M. (2021). Swiss-judgment-prediction: A multilingual legal judgment prediction benchmark. arXiv preprint arXiv:2110.00806.
  • Niklaus, J., Stürmer, M., Chalkidis, I. (2022). An Empirical Study on Cross-X Transfer for Legal Judgment Prediction. arXiv preprint arXiv:2209.12325.
  • Ribeiro, M. T., Singh, S., Guestrin, C. (2016). Model-agnostic interpretability of machine learning. arXiv preprint arXiv:1606.05386.
  • Rodríguez-Pérez, R., Bajorath, J. (2020). Interpretation of machine learning models using shapley values: application to compound potency and multi-target activity predictions. Journal of computer-aided molecular design, 34, 1013-1026.
  • Rokach, L. (2016). Decision forest: Twenty years of research. Information Fusion, 27, 111-125.
  • Roll, I., Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582-599.
  • Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., Brenning, A. (2019). Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecological Modelling, 406, 109-120.
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There are 78 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Articles
Authors

Tülay Turan 0000-0002-0888-0343

Ecir Küçüksille 0000-0002-3293-9878

Nazan Kemaloğlu Alagöz 0000-0002-6262-4244

Early Pub Date September 30, 2023
Publication Date September 30, 2023
Acceptance Date September 15, 2023
Published in Issue Year 2023 Volume: 7 Issue: 2

Cite

APA Turan, T., Küçüksille, E., & Kemaloğlu Alagöz, N. (2023). Prediction of Turkish Constitutional Court Decisions with Explainable Artificial Intelligence. Bilge International Journal of Science and Technology Research, 7(2), 128-141. https://doi.org/10.30516/bilgesci.1317525