A systematic literature review of machine learning methods in predicting court decisions

Rosili, Nur Aqilah Khadijah and Zakaria, Noor Hidayah and Hassan, Rohayanti and Kasim, Shahreen and Che Rose, Farid Zamani and Sutikno, Tole (2021) A systematic literature review of machine learning methods in predicting court decisions. IAES International Journal of Artificial Intelligence, 10 (4). pp. 1091-1102. ISSN 2089-4872

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Official URL: http://dx.doi.org/10.11591/IJAI.V10.I4.PP1091-1102


Envisaging legal cases’ outcomes can assist the judicial decision-making process. Prediction is possible in various cases, such as predicting the outcome of construction litigation, crime-related cases, parental rights, worker types, divorces, and tax law. the machine learning methods can function as support decision tools in the legal system with artificial intelligence’s advancement. This study aimed to impart a systematic literature review (SLR) of studies concerning the prediction of court decisions via machine learning methods. The review determines and analyses the machine learning methods used in predicting court decisions. This review utilised RepOrting Standards for Systematic Evidence Syntheses (ROSES) publication standard. Subsequently, 22 relevant studies that most commonly predicted the judgement results involving binary classification were chosen from significant databases: Scopus and Web of Sciences. According to the SLR’s outcomes, various machine learning methods can be used in predicting court decisions. Additionally, the performance is acceptable since most methods achieved more than 70% accuracy. Nevertheless, improvements can be made on the types of judicial decisions predicted using the existing machine learning methods.

Item Type:Article
Uncontrolled Keywords:machine learning, predicting court decision, predictive model, ROSES
Subjects:Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ID Code:95826
Deposited By: Yanti Mohd Shah
Deposited On:19 Jun 2022 11:01
Last Modified:19 Jun 2022 11:01

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