## 1 Introduction
Specifically, they focus on encoding and decoding architecture using the attention mechanism of the transformer model. In a previously mentioned paper, the researchers have reviewed different previous studies on different DL models applied to the prediction of sentences in Human Rights from the European Court of Human Rights (ECHR). They also analyzed documents from the different Chinese online judicial processes. Accordingly, the authors demonstrated that AI helps reduce procedural deadlines by 70%. Thus, integrating hierarchical attention neural network models with the adjusted transformer concept provides efficient quality- and time-based improvement in judgment prediction. However, further investigation is required to enhance multi-label classification for complex case facts with multiple defendants and charges. Future studies that focus on summarizing legal judgments, curating legal data, and simplifying legal documents will be very useful to the legal community. Despite the significant advances in ML and DL techniques, there are still some challenges to overcome. For instance, to increase the predicting court's ruling decision accuracy [21] to improve Judicial systems. In addition to prediction, there are some concerns about the explainability of how these models make decisions and the potentially biased data used to train such models [23; 24]. Consequently, using ML techniques to predict decision rules requires explanations about the features considered in making the decision. Furthermore, litigants have the right to receive explanations of the decision made. Thus, [PERSON] et al. [25] describe the different techniques to explain the law, such as the explanation by example, explanation using rules, and hybrid systems. The authors emphasize the excellent performance and the need for new techniques to explain DL based on techniques for ruling prediction. Regarding fairness, [PERSON] and [PERSON] [26] present a literature review to measure and avoid biases in the dataset for learning, such as biases due to missing data, biases from algorithm objectives and biases due to the use of proxy attributes instead of sensitive private data (e.g., gender, race, etc.). This study reviews the most recent literature on ML- and DL-based techniques for rule decision prediction. In addition, the text representation techniques used for transforming legal text into a suitable form input to the learning algorithms are examined. Thus, we divide the reported studies based on the different types of law, such as Criminal Law, Administrative Law, Constitutional Law, Human Rights and Private Law, as shown in Figure 1, which depicts the classification of the branches of public and private law. In the reviewed studies, the viability of using these technological tools to predict the occurrence of illicit acts, administrative infractions, or Human Rights violations was demonstrated. The remainder of this study is organized as follows. First, Section 2 describes the methodology for collecting scientific studies. Section 3 demonstrates related studies focusing on five main topics. Then, Section 4 describes the analysis of the techniques, representation models, and metrics for judicial decision predictions. Section 5 discusses the  
Figure 1: Survey classification by types of law. main findings of the literature review. Finally, Section 6 concludes the study and discusses future research directions.