Research Article

Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence

Volume: 14 Number: 2 June 27, 2025
EN TR

Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence

Abstract

The digitization of legal texts and advances in information processing theories and technologies have triggered several transformations in both the practice and teaching of law in recent years. Techniques developed in areas such as artificial intelligence, natural language processing, text mining, and machine learning have drawn the attention of legal practitioners and academics to this field. It is possible to remove obstacles to access to justice, improve legal security and certainty, and solve practical problems faced by legal practitioners by employing assistive tools to be created by using artificial intelligence technologies in the field of law. This study aims to develop an algorithm to predict the results of the Constitutional Court of the Republic of Türkiye on individual applications in terms of admissibility and whether there is a violation of rights by using machine learning and natural language processing techniques. In the study, the texts in the "Facts" title of the reference texts were used. A success rate of 91.56% was achieved for admissibility and 97.18% for whether there was a violation of rights. The study is unique in its field in that it performs a two-stage prediction task regarding admissibility and merit, provides a highly representative model since it includes all processable data, does not use a data augmentation method, and has a high success rate.

Keywords

Supporting Institution

Scientific and Technological Research Council of Turkey (TUBITAK)

Project Number

122G019

Thanks

This study was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) through the Scientific and Technological Research Projects Support Program (3005) with the project number 122G019.

References

  1. Davies G. The Relationship between Empirical Legal Studies and Doctrinal Legal Research. Erasmus Law Review. 2020;13(2):3–12. doi:10.5553/ELR.000141.
  2. Frankenreiter J, Livermore MA. Computational Methods in Legal Analysis. Annual Review of Law and Social Science. 2020;16(1):39–57. doi:10.1146/annurev-lawsocsci-052720-121843.
  3. Hutchinson T, Duncan N. Defining and Describing What We Do: Doctrinal Legal Research. Deakin Law Review. 2012;17(1):83–120.
  4. Kazmierski V. How Much “Law” in Legal Studies? Approaches to Teaching Legal Research and Doctrinal Analysis in a Legal Studies Program. Canadian Journal of Law and Society. 2014;29(3):297–310.
  5. Taekema S. Methodologies of Rule of Law Research: Why Legal Philosophy Needs Empirical and Doctrinal Scholarship. Law and Philosophy. 2021;40(1):33–66. doi:10.1007/s10982-020-09388-1.
  6. Alarie B, Niblett A, Yoon AH. How artificial intelligence will affect the practice of law. University of Toronto Law Journal. 2018;68(Suppl 1):106–24. doi:10.3138/utlj.2017-0052.
  7. Mumcuoğlu E, Öztürk CE, Ozaktas HM, Koç A. Natural Language Processing in Law: Prediction of Outcomes in the Higher Courts of Turkey. Information Processing & Management. 2021;58(5):102684. doi:10.1016/j.ipm.2021.102684.
  8. Medvedeva M, Vols M, Wieling M. Using Machine Learning to Predict Decisions of the European Court of Human Rights. Artificial Intelligence and Law. 2020;28(2):237–66. doi:10.1007/s10506-019-09255-y.

Details

Primary Language

English

Subjects

Information Systems Development Methodologies and Practice

Journal Section

Research Article

Publication Date

June 27, 2025

Submission Date

January 12, 2025

Acceptance Date

June 4, 2025

Published in Issue

Year 2025 Volume: 14 Number: 2

APA
Aydemir, E., Kaçar, Y., Cebeci, H. İ., & Kayacık, G. (2025). Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence. Türk Doğa Ve Fen Dergisi, 14(2), 225-239. https://doi.org/10.46810/tdfd.1618491
AMA
1.Aydemir E, Kaçar Y, Cebeci Hİ, Kayacık G. Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence. TJNS. 2025;14(2):225-239. doi:10.46810/tdfd.1618491
Chicago
Aydemir, Emrah, Yusuf Kaçar, Halil İbrahim Cebeci, and Görkem Kayacık. 2025. “Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence”. Türk Doğa Ve Fen Dergisi 14 (2): 225-39. https://doi.org/10.46810/tdfd.1618491.
EndNote
Aydemir E, Kaçar Y, Cebeci Hİ, Kayacık G (June 1, 2025) Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence. Türk Doğa ve Fen Dergisi 14 2 225–239.
IEEE
[1]E. Aydemir, Y. Kaçar, H. İ. Cebeci, and G. Kayacık, “Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence”, TJNS, vol. 14, no. 2, pp. 225–239, June 2025, doi: 10.46810/tdfd.1618491.
ISNAD
Aydemir, Emrah - Kaçar, Yusuf - Cebeci, Halil İbrahim - Kayacık, Görkem. “Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence”. Türk Doğa ve Fen Dergisi 14/2 (June 1, 2025): 225-239. https://doi.org/10.46810/tdfd.1618491.
JAMA
1.Aydemir E, Kaçar Y, Cebeci Hİ, Kayacık G. Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence. TJNS. 2025;14:225–239.
MLA
Aydemir, Emrah, et al. “Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence”. Türk Doğa Ve Fen Dergisi, vol. 14, no. 2, June 2025, pp. 225-39, doi:10.46810/tdfd.1618491.
Vancouver
1.Emrah Aydemir, Yusuf Kaçar, Halil İbrahim Cebeci, Görkem Kayacık. Prediction of Turkish Constitutional Court Decisions in Terms of Admissibility and Violation of Rights With Artificial Intelligence. TJNS. 2025 Jun. 1;14(2):225-39. doi:10.46810/tdfd.1618491

This work is licensed under the Creative Commons Attribution-Non-Commercial-Non-Derivable 4.0 International License.