## 1 Introduction
Accordingly, technology is already transforming the justice system. For instance, in British Columbia, Canada, a new Civil Dispute Tribunal is intended to operate using an online platform to allow disputants to make initial contact and starts proceedings online. Furthermore, the Northern Ireland Courts and Tribunal Service now provides an online process for small claims. The UK Civil Justice Council ('CJC') report on 'Online Dispute Resolution for Low-Value Civil Claims' recommends the establishment of a new dedicated internet-based court service for civil disputes worth less than GBP 25,000. Furthermore, the AI Solicitor collects data and analyzes relevant case law (ROSS intelligence). Another example is the Estonian Ministry of Justice, which created a robot judge capable of resolving disputes of less than EUR 7000. Consequently, AI-based judgment is more likely to be used in small cases (Birtish and Irish, 2022; Birtish and Irish, 2022). Therefore, the advantages of implementing such technologies include reduced case complexity and assistance with advice and suggestions (Birtish and Irish, 2022); courts can use AI to multiply the workforce to achieve more work with limited resources (Birtish and Irish, 2022). Thus, Judgebot reduces individual human biases and guarantees the impartiality of the process (Birtish and Irish, 2022). Consequently, implementing AI in the legal domain will be the next breakthrough in the legal domain. Furthermore, robot judges will change how humans and society make decisions (Birtish and Irish, 2022). Nevertheless, interpretability and fairness still exist. The former is concerned with the explanation of AI black-box models in the process of judgment. The latter is concerned with model learning over biased data, resulting in discriminatory models. AI is used to improve public services decision-making, particularly, to manage many citizen contacts, identify financial reporting fraud, and enable governmental responses in assessing cyber-attacks ([PERSON] and Irish, 2022). However, some challenges regarding bias in AI due to incomplete and inaccurate data reflecting historical structural inequalities exist. Furthermore, the opaqueness created by the Machine Learning (ML) algorithm's internal rules determining the input to output is a challenge. Finally, when an AI decision is made (the machine, the creators of the machine, the coders, the managers who decided to deploy the machine), responsibility or liability must be attributed. ML techniques have also been used to combat crime. The survey of [PERSON] et al. ([PERSON] et al., 2019) summarized the proportion of used learning techniques, such as unsupervised learning (38.53%), supervised learning (30.28%) and association rules (17.43%). In addition, the authors reported the most used algorithms such as K-Means (14.39%), K-Nearest Neighbors (KNN) (11.36%) and Apriori (9.85%). Based on the legal technology domain, [PERSON] et al. ([PERSON] et al., 2019) present a survey focused on legal research and predictive analytics, as well as litigation data mining. The authors showed the proportion of studies produced in different regions, such as Asia (50%), Europe (31%), North America (11%), South America (6%) and Africa (2%). Accordingly, these studies focus more on ruling prediction using supervised learning, such as Artificial Neural Networks (ANNs) and Deep Learning (DL). In addition, the authors highlight all the challenges in structuring raw legal text for consumption by a supervised learning algorithm using NLPtechniques. In the same vein, [PERSON] et al. [21] survey gathered studies on court decision prediction in the literature. They used the publication standard Reporting Standards for Systematic Evidence Syntheses (ROSES) to collect different studies. The authors conclude that the Support Vector Machine (SVM) is the most used algorithm for this problem, with an accuracy of 80-91%. [PERSON] and [PERSON] [22] recently analyzed attention models on the ruling decision prediction.