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Zhong, Haoxi, Yuzhong Wang, Cunchao Tu, Tianyang Zhang, Zhiyuan Liu, and Maosong Sun. "Iteratively Questioning and Answering for Interpretable Legal Judgment Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1250–57. http://dx.doi.org/10.1609/aaai.v34i01.5479.
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Legal Judgment Prediction (LJP) aims to predict judgment results according to the facts of cases. In recent years, LJP has drawn increasing attention rapidly from both academia and the legal industry, as it can provide references for legal practitioners and is expected to promote judicial justice. However, the research to date usually suffers from the lack of interpretability, which may lead to ethical issues like inconsistent judgments or gender bias. In this paper, we present QAjudge, a model based on reinforcement learning to visualize the prediction process and give interpretable judgments. QAjudge follows two essential principles in legal systems across the world: Presumption of Innocence and Elemental Trial. During inference, a Question Net will select questions from the given set and an Answer Net will answer the question according to the fact description. Finally, a Predict Net will produce judgment results based on the answers. Reward functions are designed to minimize the number of questions asked. We conduct extensive experiments on several real-world datasets. Experimental results show that QAjudge can provide interpretable judgments while maintaining comparable performance with other state-of-the-art LJP models. The codes can be found from https://github.com/thunlp/QAjudge.
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Chen, Junyi, Lan Du, Ming Liu, and Xiabing Zhou. "Mulan: A Multiple Residual Article-Wise Attention Network for Legal Judgment Prediction." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (2022): 1–15. http://dx.doi.org/10.1145/3503157.
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Legal judgment prediction (LJP) is used to predict judgment results based on the description of individual legal cases. In order to be more suitable for actual application scenarios in which the case has cited multiple articles and has multiple charges, we formulate legal judgment prediction as a multiple label learning problem and present a deep learning model that can effectively encode the content of each legal case via a multi-residual convolution neural network and the semantics of law articles via an article encoder. An article-wise attention mechanism is proposed to fuse the two types of encoded information. Experimental results derived on the CAIL2018 datasets show that our model provides a significant performance improvement over the existing neural models in predicting relevant law articles and charges.
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Shang, Xuerui. "A Computational Intelligence Model for Legal Prediction and Decision Support." Computational Intelligence and Neuroscience 2022 (June 24, 2022): 1–8. http://dx.doi.org/10.1155/2022/5795189.
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Legal judgment prediction (LJP) and decision support aim to enable machines to predict the verdict of legal cases after reading the description of facts, which is an application of artificial intelligence in the legal field. This paper proposes a legal judgment prediction model based on process supervision for the sequential dependence of each subtask in the legal judgment prediction task. Experimental results verify the effectiveness of the model framework and process monitoring mechanism adopted in this model. First, the convolutional neural network (CNN) algorithm was used to extract text features, and the principal component analysis (PCA) algorithm was used to reduce the dimension of data features. Next, the prediction model based on process supervision is proposed for the first time. When modeling the dependency relationship between sequential sub-data sets, process supervision is introduced to ensure the accuracy of the obtained dependency information, and genetic algorithm (GA) is introduced to optimize the parameters so as to improve the final prediction performance. Compared to our benchmark method, our algorithm achieved the best results on four different legal open data sets (CAIL2018_Small, CAIL2018_Large, CAIL2019_Small, and CAIL2019_Large). The realization of automatic prediction of legal judgment can not only assist judges, lawyers, and other professionals to make more efficient legal judgment but also provide legal aid for people who lack legal expertise.
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Zhu, Kongfan, Rundong Guo, Weifeng Hu, Zeqiang Li, and Yujun Li. "Legal Judgment Prediction Based on Multiclass Information Fusion." Complexity 2020 (October 26, 2020): 1–12. http://dx.doi.org/10.1155/2020/3089189.
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Legal judgment prediction (LJP), as an effective and critical application in legal assistant systems, aims to determine the judgment results according to the information based on the fact determination. In real-world scenarios, to deal with the criminal cases, judges not only take advantage of the fact description, but also consider the external information, such as the basic information of defendant and the court view. However, most existing works take the fact description as the sole input for LJP and ignore the external information. We propose a Transformer-Hierarchical-Attention-Multi-Extra (THME) Network to make full use of the information based on the fact determination. We conduct experiments on a real-world large-scale dataset of criminal cases in the civil law system. Experimental results show that our method outperforms state-of-the-art LJP methods on all judgment prediction tasks.
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Gan, Leilei, Kun Kuang, Yi Yang, and Fei Wu. "Judgment Prediction via Injecting Legal Knowledge into Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (2021): 12866–74. http://dx.doi.org/10.1609/aaai.v35i14.17522.
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Legal Judgment Prediction (LJP) is a key problem in legal artificial intelligence, which is aimed to predict a law case's judgment based on a given text describing the facts of the law case. Most of the previous work treats LJP as a text classification task and generally adopts deep neural networks (DNNs) based methods to solve it. However, existing DNNs based work is data-hungry and hard to explain which legal knowledge is based on to make such a prediction. Thus, injecting legal knowledge into neural networks to interpret the model and improve performance remains a significant problem. In this paper, we propose to represent declarative legal knowledge as a set of first-order logic rules and integrate these logic rules into a co-attention network-based model explicitly. The use of logic rules enhances neural networks with explicit logical reason capabilities and makes the model more interpretable. We take the civil loan scenario as a case study and demonstrate the effectiveness of the proposed method through comprehensive experiments and analysis conducted on the collected dataset.
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Lyu, Yougang, Zihan Wang, Zhaochun Ren, et al. "Improving legal judgment prediction through reinforced criminal element extraction." Information Processing & Management 59, no. 1 (2022): 102780. http://dx.doi.org/10.1016/j.ipm.2021.102780.
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Ma, Wenqing. "Artificial Intelligence-Assisted Decision-Making Method for Legal Judgment Based on Deep Neural Network." Mobile Information Systems 2022 (October 11, 2022): 1–9. http://dx.doi.org/10.1155/2022/4636485.
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With the arrival of the third revolution of artificial intelligence, the applications of artificial intelligence in the fields of automatic driving, image recognition, smart home, machine translation, medical services, e-sports, and so on can be seen everywhere, and topics about artificial intelligence are constantly emerging. Since 2017, the discussion on artificial intelligence in the field of law has become more and more active. In this context, the application of artificial intelligence in the field of legal judgment and the hypothetical system based on this technology in court judgment has also become the object of discussion from time to time. In this paper, based on the artificial intelligence decision-making method of the deep neural network, aiming at the three subtasks of legal judgment prediction, namely, crime prediction, law recommendation, and sentence prediction, a multi-task judgment prediction model BERT12multi and a sentence interval prediction model BERT-Text CNN are proposed, which improve the prediction accuracy and adopt the knowledge distillation strategy to compress the model parameters and improve the reasoning speed of the judgment model. Experiments on the CAIL2018 data set show that the performance of the deep neural network model in crime prediction and law recommendation tasks can be significantly improved by adopting the pre training model adaptive training, grouping focus loss, and gradient confrontation training strategies. Using a step-by-step sentence prediction strategy can realize the weight sharing of pre training model and make use of the prediction results of charges and laws in sentence prediction. The recall training-prediction strategy can avoid error accumulation and improve the accuracy of sentence prediction. By integrating the artificial intelligence decision-making method, the case reasoning speed can be greatly improved, the highest compressible model volume can be about 11% of the original one, and the reasoning speed can be increased by about 8 times. At the same time, performance close to that of the deep neural model can be obtained, which is superior to other legal decision prediction models based on word embedding.
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Zhang, Hu, Bangze Pan, and Ru Li. "Legal Judgment Elements Extraction Approach with Law Article-aware Mechanism." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 3 (2022): 1–15. http://dx.doi.org/10.1145/3485244.
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Legal judgment elements extraction (LJEE) aims to identify the different judgment features from the fact description in legal documents automatically, which helps to improve the accuracy and interpretability of the judgment results. In real court rulings, judges usually need to scan both the fact descriptions and the law articles repeatedly to find out the relevant information, and it is hard to acquire the key judgment features quickly, so legal judgment elements extraction is a crucial and challenging task for legal judgment prediction. However, most existing methods follow the text classification framework, which fails to model the attentive relations of the law articles and the legal judgment elements. To address this issue, we simulate the working process of human judges, and propose a legal judgment elements extraction method with a law article-aware mechanism, which captures the complex semantic correlations of the law article and the legal judgment elements. Experimental results show that our proposed method achieves significant improvements than other state-of-the-art baselines on the element recognition task dataset. Compared with the BERT-CNN model, the proposed “All labels Law Articles Embedding Model (ALEM)” improves the accuracy, recall, and F1 value by 0.5, 1.4 and 1.0, respectively.
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Li, Shang, Hongli Zhang, Lin Ye, Xiaoding Guo, and Binxing Fang. "MANN: A Multichannel Attentive Neural Network for Legal Judgment Prediction." IEEE Access 7 (2019): 151144–55. http://dx.doi.org/10.1109/access.2019.2945771.
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He, Congqing, Tien-Ping Tan, Xiaobo Zhang, and Sheng Xue. "Knowledge-Enriched Multi-Cross Attention Network for Legal Judgment Prediction." IEEE Access 11 (2023): 87571–82. http://dx.doi.org/10.1109/access.2023.3305259.
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Hong, Yu-Xiang, and Chia-Hui Chang. "Improving colloquial case legal judgment prediction via abstractive text summarization." Computer Law & Security Review 51 (November 2023): 105863. http://dx.doi.org/10.1016/j.clsr.2023.105863.
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Guo, Xiaoding, Hongli Zhang, Lin Ye, and Shang Li. "RnRTD: Intelligent Approach Based on the Relationship-Driven Neural Network and Restricted Tensor Decomposition for Multiple Accusation Judgment in Legal Cases." Computational Intelligence and Neuroscience 2019 (July 7, 2019): 1–18. http://dx.doi.org/10.1155/2019/6705405.
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The use of intelligent judgment technology to assist in judgment is an inevitable trend in the development of judgment in contemporary social legal cases. Using big data and artificial intelligence technology to accurately determine multiple accusations involved in legal cases is an urgent problem to be solved in legal judgment. The key to solving these problems lies in two points, namely, (1) characterization of legal cases and (2) classification and prediction of legal case data. Traditional methods of entity characterization rely on feature extraction, which is often based on vocabulary and syntax information. Thus, traditional entity characterization often requires extensive energy and has poor generality, thus introducing a large amount of computation and limitation to subsequent classification algorithms. This study proposes an intelligent judgment approach called RnRTD, which is based on the relationship-driven recurrent neural network (rdRNN) and restricted tensor decomposition (RTD). We represent legal cases as tensors and propose an innovative RTD method. RTD has low dependence on vocabulary and syntax and extracts the feature structure that is most favorable for improving the accuracy of the subsequent classification algorithm. RTD maps the tensors, which represent legal cases, into a specific feature space and transforms the original tensor into a core tensor and its corresponding factor matrices. This study uses rdRNN to continuously update and optimize the constraints in RTD so that rdRNN can have the best legal case classification effect in the target feature space generated by RTD. Simultaneously, rdRNN sets up a new gate and a similar case list to represent the interaction between legal cases. In comparison with traditional feature extraction methods, our proposed RTD method is less expensive and more universal in the characterization of legal cases. Moreover, rdRNN with an RTD layer has a better effect than the recurrent neural network (RNN) only on the classification and prediction of multiple accusations in legal cases. Experiments show that compared with previous approaches, our method achieves higher accuracy in the classification and prediction of multiple accusations in legal cases, and our algorithm is more interpretable.
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Zhao, Qiang, Rundong Guo, Xiaowei Feng, et al. "Research on a Decision Prediction Method Based on Causal Inference and a Multi-Expert FTOPJUDGE Mechanism." Mathematics 10, no. 13 (2022): 2281. http://dx.doi.org/10.3390/math10132281.
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Legal judgement prediction (LJP) is a crucial part of legal AI, and its goal is to predict the outcome of a case based on the information in the description of criminal facts. This paper proposes a decision prediction method based on causal inference and a multi-expert FTOPJUDGE mechanism. First, a causal inference algorithm was adopted to process unstructured text. This process did not require very much manual intervention to better mine the information in the text. Then, a neural network dedicated to each task was set up, and a neural network that simultaneously served multiple tasks was also set up. Finally, the pre-trained language model Lawformer was used to provide knowledge for downstream tasks. By using the public data set CAIL2018 and comparing it with current mainstream decision prediction models, it was shown that the model significantly improved the performance of downstream tasks and achieved great improvements in multiple indicators. Through ablation experiments, the effectiveness and rationality of each module of the proposed model were verified. The method proposed in this study achieved reasonably good performance in legal judgment prediction, which provides a promising solution for legal judgment prediction.
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Zhao, Qihui, Tianhan Gao, Song Zhou, Dapeng Li, and Yingyou Wen. "Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles." Applied Sciences 12, no. 5 (2022): 2531. http://dx.doi.org/10.3390/app12052531.
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Legal judgment prediction (LJP) is a crucial task in legal intelligence to predict charges, law articles and terms of penalties based on case fact description texts. Although existing methods perform well, they still have many shortcomings. First, the existing methods have significant limitations in understanding long documents, especially those based on RNNs and BERT. Secondly, the existing methods are not good at solving the problem of similar charges and do not fully and effectively integrate the information of law articles. To address the above problems, we propose a novel LJP method. Firstly, we improve the model’s comprehension of the whole document based on a graph neural network approach. Then, we design a graph attention network-based law article distinction extractor to distinguish similar law articles. Finally, we design a graph fusion method to fuse heterogeneous graphs of text and external knowledge (law article group distinction information). The experiments show that the method could effectively improve LJP performance. The experimental metrics are superior to the existing state of the art.
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Zhao, Gang, Huibin Shi, and Jifa Wang. "A Grey BP Neural Network-Based Model for Prediction of Court Decision Service Rate." Computational Intelligence and Neuroscience 2022 (April 14, 2022): 1–11. http://dx.doi.org/10.1155/2022/7364375.
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The judgment service rate is an important index to reflect the fairness of the judgment of legal cases in a certain area, which is of great significance to verify the accuracy of a court judgment. In this paper, a grey neural network model combining grey system theory and BP neural network algorithm is proposed to predict the index. Analyze the judgment service rate of the court judgment system, and build a prediction system based on the completion rate, completion rate, plaintiff satisfaction, defendant satisfaction, litigation time, property preservation cycle, document delivery time, implementation information disclosure rate, and other key indicators. Through example analysis, it is proved that the combined model of the grey prediction model and BP neural network has a small error and good simulation effect on the prediction of court decision-making service rate, which can better promote the development of court and society.
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Lu, Nan, and Chuanyou Yuan. "Legal reasoning: a textual perspective on common law judicial opinions and Chinese judgments." Text & Talk 41, no. 1 (2021): 71–93. http://dx.doi.org/10.1515/text-2020-2084.
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Abstract The issue of legal reasoning has been addressed widely in legal academia and practice, but rarely considered by linguists. This paper, employing the Systemic Functional Linguistics (SFL) genre perspective and the discourse semantics system as its conceptual framework, attempts to reveal the different ways of legal reasoning of common law judicial opinions and Chinese judgments from a textual perspective. One judicial opinion of a British case and one judgment of a Chinese case are explored for comparison. The findings suggest that Chinese judgments as a legal genre, compared with its counterpart of common law judicial opinions, unfold not in waves construed by multilayered Theme-and-New structure, but in chunks establishing no prediction or consolidation. We argue that this mode of text unfolding in waves is vitally important for readers to follow the judge’s reasoning and construct a sense of fairness and justice. We suggest that the periodicity and the generic structure of common law judicial opinions would be a valuable frame of reference for the Chinese judicial reform on judgments in improving its legal reasoning.
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Yang, Shuxin, Suxin Tong, Guixiang Zhu, et al. "MVE-FLK: A multi-task legal judgment prediction via multi-view encoder fusing legal keywords." Knowledge-Based Systems 239 (March 2022): 107960. http://dx.doi.org/10.1016/j.knosys.2021.107960.
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Zheng, Min, Bo Liu, and Le Sun. "Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era." Computational Intelligence and Neuroscience 2022 (August 8, 2022): 1–6. http://dx.doi.org/10.1155/2022/8490760.
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Legal judgment prediction is the most typical application of artificial intelligence technology, especially natural language processing methods, in the judicial field. In a practical environment, the performance of algorithms is often restricted by the computing resource conditions due to the uneven computing performance of the devices. Reducing the computational resource consumption of the model and improving the inference speed can effectively reduce the deployment difficulty of the legal judgment prediction model. To improve the prediction accuracy, enhance the model inference speed, and reduce the model memory consumption, we propose a BERT knowledge distillation-based legal decision prediction model, called KD-BERT. To reduce the resource consumption in the model inference process, we use the BERT pretraining model with lower memory requirements to be the encoder. Then, the knowledge distillation strategy transfers the knowledge to the student model of the shallow transformer structure. Experiment results show that the proposed KD-BERT has the highest F1-score compared with traditional BERT models. Its inference speed is also much faster than the other BERT models.
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Yanjun Li, Yanjun Li, Huan Huang Yanjun Li, Qiang Geng Huan Huang, Xinwei Guo Qiang Geng, and Yuyu Yuan Xinwei Guo. "Fairness Measures of Machine Learning Models in Judicial Penalty Prediction." 網際網路技術學刊 23, no. 5 (2022): 1109–16. http://dx.doi.org/10.53106/160792642022092305019.
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<p>Machine learning (ML) has been widely adopted in many software applications across domains. However, accompanying the outstanding performance, the behaviors of the ML models, which are essentially a kind of black-box software, could be unfair and hard to understand in many cases. In our human-centered society, an unfair decision could potentially damage human value, even causing severe social consequences, especially in decision-critical scenarios such as legal judgment. Although some existing works investigated the ML models in terms of robustness, accuracy, security, privacy, quality, etc., the study on the fairness of ML is still in the early stage. In this paper, we first proposed a set of fairness metrics for ML models from different perspectives. Based on this, we performed a comparative study on the fairness of existing widely used classic ML and deep learning models in the domain of real-world judicial judgments. The experiment results reveal that the current state-of-the-art ML models could still raise concerns for unfair decision-making. The ML models with high accuracy and fairness are urgently demanding.</p> <p>&nbsp;</p>
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Hsieh, Decheng, Lieuhen Chen, and Taiping Sun. "Legal Judgment Prediction Based on Machine Learning: Predicting the Discretionary Damages of Mental Suffering in Fatal Car Accident Cases." Applied Sciences 11, no. 21 (2021): 10361. http://dx.doi.org/10.3390/app112110361.
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The discretionary damage of mental suffering in fatal car accident cases in Taiwan is subjective, uncertain, and unpredictable; thus, plaintiffs, defendants, and their lawyers find it difficult to judge whether spending much of their money and time on the lawsuit is worthwhile and which legal factors judges will consider important and dominant when they are assessing the mental suffering damages. To address these problems, we propose k-nearest neighbor, classification and regression trees, and random forests as learning algorithms for regression to build optimal predictive models. In addition, we reveal the importance ranking of legal factors by permutation feature importance. The experimental results show that the random forest model outperformed the other models and achieved good performance, and “the mental suffering damages that plaintiff claims” and “the age of the victim” play important roles in assessments of mental suffering damages in fatal car accident cases in Taiwan. Therefore, litigants and their lawyers can predict the discretionary damages of mental suffering in advance and wisely decide whether they should litigate or not, and then they can focus on the crucial legal factors and develop the best litigation strategy.
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Mao, Xuetao, Wei Duan, Lin Li, and Jianwei Zhang. "A prison term prediction model based on fact descriptions by capturing long historical information." Web Intelligence 19, no. 1-2 (2021): 103–13. http://dx.doi.org/10.3233/web-210459.
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The legal judgments are always based on the description of the case, the legal document. However, retrieving and understanding large numbers of relevant legal documents is a time-consuming task for legal workers. The legal judgment prediction (LJP) focus on applying artificial intelligence technology to provide decision support for legal workers. The prison term prediction(PTP) is an important task in LJP which aims to predict the term of penalty utilizing machine learning methods, thus supporting the judgement. Long-Short Term Memory(LSTM) Networks are a special type of Recurrent Neural Networks(RNN) that are capable of handling long term dependencies without being affected by an unstable gradient. Mainstream RNN models such as LSTM and GRU can capture long-distance correlation but training is time-consuming, while traditional CNN can be trained in parallel but pay more attention to local information. Both have shortcomings in case description prediction. This paper proposes a prison term prediction model for legal documents. The model adds causal expansion convolution in general TextCNN to make the model not only limited to the most important keyword segment, but also focus on the text near the key segments and the corresponding logical relationship of this paragraph, thereby improving the predicting effect and the accuracy on the data set. The causal TextCNN in this paper can understand the causal logical relationship in the text, especially the relationship between the legal text and the prison term. Since the model uses all CNN convolutions, compared with traditional sequence models such as GRU and LSTM, it can be trained in parallel to improve the training speed and can handling long term. So causal convolution can make up for the shortcomings of TextCNN and RNN models. In summary, the PTP model based on causality is a good solution to this problem. In addition, the case description is usually longer than traditional natural language sentences and the key information related to the prison term is not limited to local words. Therefore, it is crucial to capture substantially longer memory for LJP domains where a long history is required. In this paper, we propose a Causality CNN-based Prison Term Prediction model based on fact descriptions, in which the Causal TextCNN method is applied to build long effective history sizes (i.e., the ability for the networks to look very far into the past to make a prediction) using a combination of very deep networks (augmented with residual layers) and dilated convolutions. The experimental results on a public data show that the proposed model outperforms several CNN and RNN based baselines.
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Park, Minjung, and Sangmi Chai. "AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion Cases." Applied Sciences 11, no. 23 (2021): 11080. http://dx.doi.org/10.3390/app112311080.
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Since there are growing concerns regarding online privacy, firms may have the risk of being involved in various privacy infringement cases resulting in legal causations. If firms are aware of consequences from possible cases of invasion of online privacy, they can more actively prevent future online privacy infringements. Thus, this study attempts to predict the probability of judgment types caused by various invasions within US judicial cases that are related to online privacy invasions. Since legal judgment results are significantly influenced by societal factors and technological development, this study tries to identify a model that can accurately predict legal judgment with explainability. To archive the study objective, it compares the prediction performance by applying five types of classification algorithms (LDA, NNET, CART, SVM, and random forest) of machine learning. We also examined the relationship between privacy infringement factors and adjudications by applying network text analysis. The results indicate that firms could have a high possibility of both civil and criminal law responsibilities if they distributed malware or spyware, intentionally or non-intentionally, to collect unauthorized data. It addresses the needs of reflecting both quantitative and qualitative approach for establishing automatic legal systems for improving its accuracy based on the socio-technical perspective.
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Intelligence and Neuroscience, Computational. "Retracted: Study of Deep Learning-Based Legal Judgment Prediction in Internet of Things Era." Computational Intelligence and Neuroscience 2023 (July 12, 2023): 1. http://dx.doi.org/10.1155/2023/9856834.
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Chen, Junyi, Xuanqing Zhang, Xiabing Zhou, Yingjie Han, and Qinglei Zhou. "An Approach Based on Cross-Attention Mechanism and Label-Enhancement Algorithm for Legal Judgment Prediction." Mathematics 11, no. 9 (2023): 2032. http://dx.doi.org/10.3390/math11092032.
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Legal Judgment Prediction aims to automatically predict judgment outcomes based on descriptions of legal cases and established law articles, and has received increasing attention. In the preliminary work, several problems still have not been adequately solved. One is how to utilize limited but valuable label information. Existing methods mostly ignore the gap between the description of established articles and cases, but directly integrate them. Second, most studies ignore the mutual constraint among the subtasks, such as logically or semantically, each charge is only related to some specific articles. To address these issues, we first construct a crime similarity graph and then perform a distillation operation to collect discriminate keywords for each charge. Furthermore, we fuse these discriminative keywords instead of established article descriptions into case embedding with a cross-attention mechanism to obtain deep semantic representations of cases incorporating label information. Finally, under a constraint among subtasks, we optimize the one-hot representation of ground-truth labels to guarantee consistent results across the subtasks based on the label-enhancement algorithm. To verify the effectiveness and robustness of our framework, we conduct extensive experiments on two public datasets. The experimental results show that the proposed method outperforms the state-of-art models by 3.89%/7.92% and 1.23%/2.50% in the average MF1-score of the subtasks on CAIL-Small/Big, respectively.
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Goto, Tetsuji, Ryo Hatano, and Satoshi Tojo. "Dynamic Epistemic Reasoning with Awareness and Its Legal Application." Vietnam Journal of Computer Science 06, no. 01 (2019): 29–42. http://dx.doi.org/10.1142/s2196888819500064.
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Concerning a software tool of legal reasoning, it is important to describe the prediction about the result of a criminal action, because a crime is often caused by the unpredictability of the result of the defendant. In the court, the judge needs to investigate the predictability and the intention of the agent. Previously, we have formalized the reasoning process of judgment by action model in dynamic epistemic logic (DEL) and have attempted to describe the precedents. However, the prediction in legal cases depends not only on the states of knowledge but also on the limited degree of attention by agents. In this paper, we employ DEL with awareness for multi-agent to represent the predictability and model the typical criminal precedents. We propose a revised semantics of action model with awareness which can define each basic action model to reproduce the agent’s considering process. To describe the legal reasoning we introduce an extension of modeling program DEMO to include the awareness (we call it DEMO[Formula: see text]) and present a GUI in this extended program to calculate the updated epistemic model easily and to classify precedents according to the degree of prediction. In the end, we calculate the epistemic models of typical criminal precedents by this newly developed tool and estimate them.
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Tagny-Ngompé, Gildas, Stéphane Mussard, Guillaume Zambrano, Sébastien Harispe, and Jacky Montmain. "Identification of Judicial Outcomes in Judgments: A Generalized Gini-PLS Approach." Stats 3, no. 4 (2020): 427–43. http://dx.doi.org/10.3390/stats3040027.
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This paper presents and compares several text classification models that can be used to extract the outcome of a judgment from justice decisions, i.e., legal documents summarizing the different rulings made by a judge. Such models can be used to gather important statistics about cases, e.g., success rate based on specific characteristics of cases’ parties or jurisdiction, and are therefore important for the development of Judicial prediction not to mention the study of Law enforcement in general. We propose in particular the generalized Gini-PLS which better considers the information in the distribution tails while attenuating, as in the simple Gini-PLS, the influence exerted by outliers. Modeling the studied task as a supervised binary classification, we also introduce the LOGIT-Gini-PLS suited to the explanation of a binary target variable. In addition, various technical aspects regarding the evaluated text classification approaches which consists of combinations of representations of judgments and classification algorithms are studied using an annotated corpora of French justice decisions.
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Guo, Xiaoding, Hongli Zhang, Lin Ye, Shang Li, and Guangyao Zhang. "TenRR: An Approach Based on Innovative Tensor Decomposition and Optimized Ridge Regression for Judgment Prediction of Legal Cases." IEEE Access 8 (2020): 167914–29. http://dx.doi.org/10.1109/access.2020.2999522.
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Zhao, Qihui, Tianhan Gao, and Nan Guo. "LA-MGFM: A legal judgment prediction method via sememe-enhanced graph neural networks and multi-graph fusion mechanism." Information Processing & Management 60, no. 5 (2023): 103455. http://dx.doi.org/10.1016/j.ipm.2023.103455.
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Yari, Ayatollah, and Hossein Mirmohammad Sadeghi. "Legal Basis of Common Approaches to Object to a Criminal Judgment in Iran and England Penal Systems." Journal of Politics and Law 11, no. 1 (2018): 1. http://dx.doi.org/10.5539/jpl.v11n1p1.
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One of the basic discussions in criminal procedure code which has a direct relation with defendants’ rights in civil procedure process is the matter of objection to criminal judgments that have seriously changed and transformed after the Islamic Revolution. According to the criticisms received by Iran's legal procedure system, the legislator has tried to make closer their position to the world’s standards in the field of objection to criminal judgments by referring to its former rules especially the law of criminal trials’ principles in the law of criminal procedure code approved in 2013. In addition to the final nature of the sentences in common law system, today, different ways of objection are predicted in England accusatory system. The present research tries to deal with the matter that on the prediction of common ways of objection how much its legal basis is considered and how much Iran and England legislators succeed in this path, in addition to analyzing the real examples of the ordinary ways of projection (objection, research appeal, and review appeal) and legal foundations of each one of them in two penal systems of Iran and England. The results of the cases above can be the guide of Iran's legislator in approving and reforming the regulations related to the objection the votes and approximating the regulations to world’s criteria in this field.
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Rhee, Gina S. "Artificial Intelligence Prediction Program in Criminal Justice System: focused on its Biased Algorithm in relation to the Racial Discrimination." Wonkwang University Legal Research Institute 39, no. 2 (2023): 57–73. http://dx.doi.org/10.22397/wlri.2023.39.2.57.
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In recent period, crime prediction programs have been newly introduced and utilized internationally in the field of criminal justice. COMPAS (“Corrective Offender Management Profiling for Alternative Sanctions”), as a representative example, is a recidivism prediction program used in several States in the United States. COMPAS is the most widely used risk assessment tools in the United States. The U.S. company Northpointe has developed an artificial intelligence algorithm that predicts the possibility of recidivism by analyzing the accumulated data such as criminal records, family relationships, educational history, drug abuse, etc. However, as it has been controversially argued that the results of these algorithms violate the defendants' constitutional rights, fundamental questions arise on how the results of the algorithm are produced, and what factors are calculated in judging a specific decision. In the era of A.I., ‘artificial intelligence’ is a concept that encompasses both technology development, utilization, and operation systems, normative judgment and policy design related to the use of the system in the judicial system. Furthermore, ethical guidelines for preventing individual risks in the use of artificial intelligence and other legal restraints, including criminal sanctions, should be established. Based on the crime prediction, this study will discuss the bias and racism of algorithms based on crime prediction technologies. This paper further aims to scrutinize the crime prediction and artificial intelligence algorithms in relation to the racial discrimination and social inequality against specific groups in criminal justice. Though not as much as in the U.S., often referred to as a ‘salad bowl’ society, South Korea has also entered a multicultural society due to recent surge in immigration, labor market, and international marriage. Lastly, the author emphasizes the importance of further research on the utilization of crime prediction tool in South Korea, as it requires careful deliberation and thorough comparative legal research prior the adoption of the new technology in the criminal justice system.
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Janus-Dębska, Anna. "The significance of resocialization diagnosis in the work of an adult probation officer." Polish Journal of Criminology 4, no. 1 (2018): 1–12. http://dx.doi.org/10.5604/01.3001.0012.5782.
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In accordance with the legal regulation of the Criminal Code and the Executive Penal Code in force in Poland, both in preparatory and executive proceedings, the court should make decisions based on an individual criminological prediction. In many European countries, reports in trial phase, pre-trial reports, as well as pre-sentence reports are prepared by probation officers. In Poland, the court often uses the help of probation officers during the enforcement phase, sporadically before the judgment is passed. Diagnosis properly prepared by a probation officer allows planning social rehabilitation interactions appropriate to the deficits and resources of the convicted person. It is important to acquire detailed knowledge about the extent of the probationer's problems and their character, which will help in the implementation of proper and effective interactions.
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Kim, Hye-Jeong. "Redefining the concept of ‘person’ in criminal law: Whether to grant legal personality to artificial intelligence systems." Kyung Hee Law Journal 57, no. 4 (2022): 71–100. http://dx.doi.org/10.15539/khlj.57.4.3.
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The development of science and technology in the 4th industrial revolution requires a change in our traditional thinking. The autonomy of artificial intelligence systems based on big data and machine learning is increasing day by day. These changes are urging a change in the basic paradigm of criminal law based on the correlation between 'free will and responsibility'. Accordingly, criminal law is facing the question of whether a new legal personhood should be recognized for artificial intelligence systems or artificial intelligence robots.
 Although the concept of artificial intelligence system does not seem to be unified yet, the European Union states that artificial intelligence system “means software implemented by a specific technique, and content and prediction through interaction with the surrounding environment within the scope of a purpose defined by humans, reasoning, and decision-making.” The representative characteristic of these AI systems is autonomy. Autonomy can be defined as “the ability to make decisions and execute them externally, independent of external influences or controls.”(Regarding who will take responsibility for the problems caused by artificial intelligence systems or artificial intelligence robots capable of autonomous judgment, the negative opinion that the ability to take responsibility cannot be acknowledged to artificial intelligence robots, and strong artificial intelligence systems rather than weak artificial intelligence systems As an autonomous subject of action, it is opposed to a positive view that it can acknowledge its responsibility.)
 It is necessary to think about whether artificial intelligence robots can be said to be human beings just as natural people are human beings, that is, whether we need to re-evaluate our ability to take responsibility for all beings. In the future, if artificial intelligence robots become more common than they are today, interactions with humans become more active, and the intellectual capabilities of artificial intelligence systems further improve, there is a political need to regulate them legally.
 The current legal system recognizes the subjectivity of rights by granting unlimited rights capacity only to natural persons, and recognizes the subjectivity of rights within a limited scope to corporations. However, since this is recognized by the provisions of the law rather than absolute, it is a legal policy issue that can vary depending on the era and society in terms of legal policy and to what extent the legal capacity or legal personality is to be granted. Although currently weak AI systems do not have full autonomy, considering the 'unpredictability' inherent in AI systems, it is necessary to concretize the discussion on granting legal personality to strong AI systems to come in the future. have.
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Haines-Delmont, Alina, Gurdit Chahal, Ashley Jane Bruen, et al. "Testing Suicide Risk Prediction Algorithms Using Phone Measurements With Patients in Acute Mental Health Settings: Feasibility Study." JMIR mHealth and uHealth 8, no. 6 (2020): e15901. http://dx.doi.org/10.2196/15901.
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Background Digital phenotyping and machine learning are currently being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve mental health risk prediction and prevention and advance mental health globally. Objective This study aimed to apply machine learning in an acute mental health setting for suicide risk prediction. This study uses a nascent approach, adding to existing knowledge by using data collected through a smartphone in place of clinical data, which have typically been collected from health care records. Methods We created a smartphone app called Strength Within Me, which was linked to Fitbit, Apple Health kit, and Facebook, to collect salient clinical information such as sleep behavior and mood, step frequency and count, and engagement patterns with the phone from a cohort of inpatients with acute mental health (n=66). In addition, clinical research interviews were used to assess mood, sleep, and suicide risk. Multiple machine learning algorithms were tested to determine the best fit. Results K-nearest neighbors (KNN; k=2) with uniform weighting and the Euclidean distance metric emerged as the most promising algorithm, with 68% mean accuracy (averaged over 10,000 simulations of splitting the training and testing data via 10-fold cross-validation) and an average area under the curve of 0.65. We applied a combined 5×2 F test to test the model performance of KNN against the baseline classifier that guesses training majority, random forest, support vector machine and logistic regression, and achieved F statistics of 10.7 (P=.009) and 17.6 (P=.003) for training majority and random forest, respectively, rejecting the null of performance being the same. Therefore, we have taken the first steps in prototyping a system that could continuously and accurately assess the risk of suicide via mobile devices. Conclusions Predicting for suicidality is an underaddressed area of research to which this paper makes a useful contribution. This is part of the first generation of studies to suggest that it is feasible to utilize smartphone-generated user input and passive sensor data to generate a risk algorithm among inpatients at suicide risk. The model reveals fair concordance between phone-derived and research-generated clinical data, and with iterative development, it has the potential for accurate discriminant risk prediction. However, although full automation and independence of clinical judgment or input would be a worthy development for those individuals who are less likely to access specialist mental health services, and for providing a timely response in a crisis situation, the ethical and legal implications of such advances in the field of psychiatry need to be acknowledged.
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YI, ZOONIL. "Artificial Intelligence and the Constitution: Response from the Perspective of Constitutional Rights to the Risks of Artificial Intelligence." Korean Constitutional Law Association 28, no. 2 (2022): 347–83. http://dx.doi.org/10.35901/kjcl.2022.28.2.347.
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Artificial intelligence (AI) is a technology that represents the 4th industrial revolution. AI, designed as a technology for realizing human natural intelligence, is being used in various fields. There is growing concern that the use of AI will increase threats to human life, such as unemployment, invasion of privacy, distribution of false information, intensification of discrimination and infringement of consumer rights. AI provides both benefits and risks to humans. AI can be associated with a variety of fundamental rights. First, from the perspective of the right to self-determination of personal information, the risk of AI being used as a monitoring tool is increasing by strengthening the collection and use of personal information. The possibility of AI infringing on the right to self-determination of personal information can be resolved through the introduction of a human rights impact assessment system or a duty to explain. Meanwhile, AI is criticized for reproducing and reinforcing discrimination. The bias included in the data or algorithms used by AI is creating bias in judgment or prediction through AI. In order to strengthen the transparency of AI, it is necessary to solve the problem of discrimination caused by AI by introducing a system such as an audit. In addition, the risk of infringing consumer rights by products using AI is also subject to criticism. In relation to the infringement of constitutional rights by a third party in the field of AI, it is necessary to utilize the legal logic of the right to seek protection of constitutional rights from the state or the legal effect of constitutional rights on third parties. Through this, sanctions against the perpetrators and remedies for the victims should be provided at the same time. In relation to AI, which is both a benefit and a risk, it would be desirable to seek alternatives through the harmony and balance of conflicting constitutional rights.
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Dal Pont, Thiago Raulino, Isabela Cristina Sabo, Jomi Fred Hübner, and Aires José Rover. "Regression applied to legal judgments to predict compensation for immaterial damage." PeerJ Computer Science 9 (March 23, 2023): e1225. http://dx.doi.org/10.7717/peerj-cs.1225.
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Immaterial damage compensation is a controversial matter in the judicial practice of several law systems. Due to a lack of criteria for its assessment, the judge is free to establish the value based on his/her conviction. Our research motivation is that knowing the estimated amount of immaterial damage compensation at the initial stage of a lawsuit can encourage an agreement between the parties. We thus investigate text regression techniques to predict the compensation value from legal judgments in which consumers had problems with airlines and claim for immaterial damage. We start from a simple pipeline and create others by adding some natural language processing (NLP) and machine learning (ML) techniques, which we call adjustments. The adjustments include N-Grams Extraction, Feature Selection, Overfitting Avoidance, Cross-Validation and Outliers Removal. An special adjustment, Addition of Attributes Extracted by the Legal Expert (AELE), is proposed as a complementary input to the case text. We evaluate the impact of adding these adjustments in the pipeline in terms of prediction quality and execution time. N-Grams Extraction and Addition of AELE have the biggest impact on the prediction quality. In terms of execution time, Feature Selection and Overfitting Avoidance have significant importance. Moreover, we notice the existence of pipelines with subsets of adjustments that achieved better prediction quality than a pipeline with them all. The result is promising since the prediction error of the best pipeline is acceptable in the legal environment. Consequently, the predictions will likely be helpful in a legal environment.
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Cherry, Miriam A. "Are Uber and Transportation Network Companies the Future of Transportation (Law) and Employment (Law)?" Texas A&M Law Review 4, no. 2 (2017): 173–95. http://dx.doi.org/10.37419/lr.v4.i2.1.
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This Article largely eschews easy or reflexive judgments about Uber or other TNCs. In this piece, the Author asks two questions about the economic, social, technical, and political aspects of TNCs and their interactions with the law. First, are Uber and TNCs the future of transportation (and transportation law)? And second, are Uber and TNCs the future of employment (and employment law)? In a common-law system, reasoning from precedent is always a form of prediction. As Oliver Wendell Holmes stated, “[t]he prophecies of what the courts will do in fact, and nothing more pretentious, are what I mean by the law.” But answering these questions is more than a legal issue. Such predictions depend on analyzing not just legal precedents but also social and economic trends. Predicting the future, especially of technology, is always a risky and fraught task. Yet drawing on trends we can see developing now, portions of the “uber” business model are here to stay, while other parts are unlikely to remain.
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Gao, Youyang, Dechun Yin, Xiaoliang Zhao, Yu Wang, and Yan Huang. "Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model." Wireless Communications and Mobile Computing 2022 (August 8, 2022): 1–16. http://dx.doi.org/10.1155/2022/3151563.
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Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy.
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Alghazzawi, Daniyal, Omaimah Bamasag, Aiiad Albeshri, Iqra Sana, Hayat Ullah, and Muhammad Zubair Asghar. "Efficient Prediction of Court Judgments Using an LSTM+CNN Neural Network Model with an Optimal Feature Set." Mathematics 10, no. 5 (2022): 683. http://dx.doi.org/10.3390/math10050683.
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As the amount of historical data available in the legal arena has grown over time, industry specialists are driven to gather, compile, and analyze this data in order to forecast court case rulings. However, predicting and justifying court rulings while using judicial facts is no easy task. Currently, previous research on forecasting court outcomes using small experimental datasets yielded a number of unanticipated predictions utilizing machine learning (ML) models and conventional methodologies for categorical feature encoding. The current work proposes forecasting court judgments using a hybrid neural network model, namely a long short-term memory (LSTM) network with a CNN, in order to effectively forecast court rulings using historic judicial datasets. By prioritizing and choosing features that scored the highest in the provided legal data set, only the most pertinent features were picked. After that, the LSTM+CNN model was utilized to forecast lawsuit verdicts. In contrast to previous related experiments, this composite model’s testing results were promising, showing 92.05 percent accuracy, 93 percent precision, 94 percent recall, and a 93 percent F1-score.
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Ribeiro, Rafael Viana. "The Quantification of Law: Counting, Predicting, and Valuating." Law, Technology and Humans 3, no. 1 (2021): 51–67. http://dx.doi.org/10.5204/lthj.1603.
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Legal reasoning is increasingly quantified. Developers in the market and public institutions in the legal system are making use of massive databases of court opinions and other legal communications to craft algorithms to assess the effectiveness of legal arguments or predict court judgments; tasks that were once seen as the exclusive province of seasoned lawyers’ obscure knowledge. New legal technologies promise to search heaps of documents for useful evidence, and to analyze dozens of factors to quantify a lawsuit’s odds of success. Legal quantification initiatives depend on the availability of reliable data about the past behavior of courts that institutional actors have attempted to control. The development of initiatives in legal quantification is visible as public bodies craft their own tools for internal use and access by the public, and private companies create new ways to valorize the “raw data” provided by courts and lawyers by generating information useful to the strategies of legal professionals, as well as to the investors that re-valorize legal activity by securitizing legal risk through litigation funding.
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Viana Ribeiro, Alice Granada. "The Quantification of Law: Counting, Predicting, and Valuating." Law, Technology and Humans 3, no. 1 (2021): 51–67. http://dx.doi.org/10.5204/lthj.1966.
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Legal reasoning is increasingly quantified. Developers in the market and public institutions in the legal system are making use of massive databases of court opinions and other legal communications to craft algorithms to assess the effectiveness of legal arguments or predict court judgments; tasks that were once seen as the exclusive province of seasoned lawyers’ obscure knowledge. New legal technologies promise to search heaps of documents for useful evidence, and to analyze dozens of factors to quantify a lawsuit’s odds of success. Legal quantification initiatives depend on the availability of reliable data about the past behavior of courts that institutional actors have attempted to control. The development of initiatives in legal quantification is visible as public bodies craft their own tools for internal use and access by the public, and private companies create new ways to valorize the “raw data” provided by courts and lawyers by generating information useful to the strategies of legal professionals, as well as to the investors that re-valorize legal activity by securitizing legal risk through litigation funding. The article The Quantification of Law: Counting, Predicting, and Valuating by Rafael Viana Ribeiro (Law, Technology and Humans, 3, no 1 (2021): 51-67. https://doi.org/10.5204/lthj.1603) was originally published on March 2, 2021. The author name has been changed at the request of the author. The correction notice can be found at https://doi.org/10.5204/lthj.1965
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HERINEAN, Dorel. "Participația penală și persoanele juridice. Istoric și perspective de viitor." Analele Universitării din București - Drept, no. 2022 (January 30, 2023): 170–217. http://dx.doi.org/10.31178/aubd.2022.11.
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The purpose of the article is to study the evolution of the criminal participation in Romania, with increased attention paid to the way in which criminal liability of legal entities was accepted and developed over time, to try to formulate predictions about the future of legal persons in the criminal law and to analyse the possibility of accepting, according to the current rules, the artificial intelligence systems as subjects of criminal law. The historical perspective is doubled by comparative law case studies which are based on judgments from the United Kingdom and France that are representative for the currents of opinion regarding need to prosecute and convict legal persons together with the individuals involved in the criminal activity.
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Greenberg, Linn Turner. "The Psychiatrist's Dilemma." Journal of Psychiatry & Law 17, no. 3 (1989): 381–411. http://dx.doi.org/10.1177/009318538901700303.
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Whether or not psychiatrists are able to assess accurately the dangerous propensities of their patients, they face liability for decisions based on these predictions. Balancing public safety and cost to liberty has been delegated to psychiatrists without adequate legal or professional standards. Some jurisdictions have some standards restricting commitment, but even where commitment is not statutorily permitted, other liability has been found. Until society is confronted with the costs of guaranteeing public safety, the status quo will continue. The author suggests that until adequate legal standards are established or immunity for discretionary judgments is granted, psychiatrists should use a very low threshold of dangerousness for a basis for protective action. By refusing to take risks, psychiatrists could effect legal reform.
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Mothukuri, Radha, Bobba Basaveswararao, and Suneetha Bulla. "Judgement Classification Using Hybrid ANN-Shuffled Frog Leaping Model on Cyber Crime Judgement Database." Revue d'Intelligence Artificielle 34, no. 4 (2020): 445–56. http://dx.doi.org/10.18280/ria.340409.
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The world has taken dramatic transformation after advent of Information Technology, it is hard to find the people without cyber connected and every activity of us is guided and regulated by the connected networks. As the world is depending upon the information technology there is same extent of research is getting on cyber monitoring activities taking place around the world. Now, it is very vital to classify and prediction of cybercrimes on the connected era. The objective of the paper is to classify the cyber crime judgments precedents for providing knowledgeable and relevant information to the cyber crime legal stakeholders. The stakeholders extract information from the precedents is a crucial research problem because so much of judgments available in a digital form with remarkable evaluation of internet and bid data analytics. It is necessary to classify the precedents and to provide a bird- eye view of the relevant legal topics. In this study cybercrime related 2500 judgments are considered for evaluation of the Feed Forward Neural - Shuffled Frog Leaping (FNN-SFL) model. To achieve this objective a Feed Forward Neural based model with tuning of Term weights by adaption of a Bio Inspired tuning model Shuffled Frog Leaping model. The experiments are conducted and implemented the newly proposed FNN-SFL algorithm. The results and discussions are presented. The conclusions and future scope are presented at the end of the paper.
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Manzo, Alejandro Gabriel. "Enforceability of judgments against sovereign States: critical analysis of the NML vs. Argentina injunction." Revista Direito GV 14, no. 2 (2018): 682–706. http://dx.doi.org/10.1590/2317-6172201826.
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Abstract Sentences against sovereign States are difficult to enforce in courts. The Court of New York, in “NML Capital Ltd. vs. Argentina” (NML), tried to solve this situation with an injunction that blocked the payments of Argentina’s sovereign debt. The specialized literature has theoretically predicted that this injunction would cause harm to third parties and problems with other States. This article empirically corroborates these predictions with the analysis of a trial derived from NML: the “Citibank Argentina” case. The analysis of this case confirms the restraints presented by the literature about the lack of proper consideration of the requirements that the American legal system imposes for the applicability of an injunction that affects third parties and operates extraterritorially. Similarly, this paper argues that there are solid legal reasons for the authorities of third countries to declare inadmissible the extraterritorial effects of an injunction, such as the one obtained by NML, when those effects fall on assets and agents located in these authorities jurisdiction.
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Aletras, Nikolaos, Dimitrios Tsarapatsanis, Daniel Preoţiuc-Pietro, and Vasileios Lampos. "Predicting judicial decisions of the European Court of Human Rights: a Natural Language Processing perspective." PeerJ Computer Science 2 (October 24, 2016): e93. http://dx.doi.org/10.7717/peerj-cs.93.
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Recent advances in Natural Language Processing and Machine Learning provide us with the tools to build predictive models that can be used to unveil patterns driving judicial decisions. This can be useful, for both lawyers and judges, as an assisting tool to rapidly identify cases and extract patterns which lead to certain decisions. This paper presents the first systematic study on predicting the outcome of cases tried by the European Court of Human Rights based solely on textual content. We formulate a binary classification task where the input of our classifiers is the textual content extracted from a case and the target output is the actual judgment as to whether there has been a violation of an article of the convention of human rights. Textual information is represented using contiguous word sequences, i.e., N-grams, and topics. Our models can predict the court’s decisions with a strong accuracy (79% on average). Our empirical analysis indicates that the formal facts of a case are the most important predictive factor. This is consistent with the theory of legal realism suggesting that judicial decision-making is significantly affected by the stimulus of the facts. We also observe that the topical content of a case is another important feature in this classification task and explore this relationship further by conducting a qualitative analysis.
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Schouten, Gina. "On meeting students where they are: Teacher judgment and the use of data in higher education." Theory and Research in Education 15, no. 3 (2017): 321–38. http://dx.doi.org/10.1177/1477878517734452.
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It is treated as a truism that teaching well requires ‘meeting students where they are’. Data enable us to know better where that is. Data can improve instructional practice by informing predictions about which pedagogies will be most successful for which students, and it can improve advising practice by informing predictions about which students are likely to thrive on which pathways moving forward. But moral hazards lurk, and these have been highlighted especially in response to the burgeoning use of new data mining technologies to produce ‘big data’. This article explores the ethics of data use in higher education. I consider the ethics of aggregate data as a tool for meeting students where they are, comparing it to an ongoing debate about the use of statistics in the legal context. The comparison generates two important insights: First, even the most viable moral concerns about using statistical information in the educational context are not deal-breakers: Those concerns should lead us to exercise careful judgment in the use of statistical information but do not justify eschewing that information altogether. Second, surprisingly, those viable moral concerns show big data to have a moral advantage over traditional little data, suggesting that some of the resistance to the use of big data in education is either unfounded or at least needs to be balanced against the moral advantages big data offer.
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Haidar, Aissa, Tarik Ahajjam, Imad Zeroual, and Yousef Farhaoui. "Application of machine learning algorithms for predicting outcomes of accident cases in Moroccan courts." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 2 (2022): 1103. http://dx.doi.org/10.11591/ijeecs.v26.i2.pp1103-1108.
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<p><span>Due to the large number of legal cases, the processing of them by the courts is generally very slow. Among these cases, we find accidents cases, which require a great speed of judgment to compensate the victims of those accidents. To this end, we thought of exploiting the possibilities offered by machine learning in order to simulate the work of judges and contribute to speeding up the time of decision. Further, we applied different machine learning algorithms, such as linear regression, decision trees, and random forests. According to the results achieved, the Random Forest is the most perfect model for with the utmost accuracy about 91.05%</span></p>
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Agamirov, K. V. "Social Sphere as an Object of Legal Regulation and Legal Forecasting." Lex Russica 1, no. 2 (2020): 106–24. http://dx.doi.org/10.17803/1729-5920.2020.159.2.106-124.
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The importance of legal forecasting lies in the study of legal phenomena and processes that occur under the influence of economic, political, demographic, ideological, and international factors of change, and in the development of proposals for the optimal development of legislation for their subsequent inclusion in legislative work plans. The main methodological problem of legal forecasting is to reveal the essence of the category "legal system and the future", the dynamics of which determines the quality of predictive research at all levels: strategies for the development of Russian legislation; legal institutions; legal education and law making; legal behavior of the individual (sociological aspect of forecasting). Representing a system of certain theoretical principles, forms and methods, as well as epistemological regularities for obtaining probabilistic judgments about the future state of legal and state phenomena and processes, the methodology of legal forecasting is aimed at improving the effectiveness of normative acts in all branches of law. It determines the most rational ways of developing the legal system as a whole. The paper analyzes the state of legal regulation in the field of maternal, child and family protection, social security, labor relations and some other areas of social reality. Using legal methods of forecasting, the author sketches the socio-legal institutional and industry models based on political-legal, socio-economic and spiritual factors, which are important landmarks to improve social relations, legal institutions and standards. The author proposes specific measures for the modernization of the legislative institutions in the socio-legal environment corresponding to the socio-cultural processes taking place in society and expected changes in the socio-cultural conditions in the future based on experienced or anticipated social needs. Conclusion: the current stage and social dynamics of social development require urgent legislative measures to ensure a decent human existence and implement the provision of article 2 of the Constitution of the Russian Federation on his rights and freedoms as the highest value.
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Mohan, Divya, and Latha Ravindran Nair. "A Robust Deep Model for Improved Categorization of Legal Documents for Predictive Analytics." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (2023): 175–83. http://dx.doi.org/10.17762/ijritcc.v11i3s.6179.
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Predictive legal analytics is a technology used to predict the chances of successful and unsuccessful outcomes in a particular case. Predictive legal analytics is performed through automated document classification for facilitating legal experts in their classification of court documents to retrieve and understand the details of specific legal factors from legal judgments for accurate document analysis. However, extracting these factors from legal texts document is a time-consuming process. In order to facilitate the task of classifying documents, a robust method namely Distributed Stochastic Keyword Extraction based Ensemble Theil-Sen Regressive Deep Belief Reweight Boost Classification (DSKE-TRDBRBC) is proposed. The DSKE-TRDBRBC technique consists of two major processes namely Keyword Extraction and Classification. At first, the t-distributed stochastic neighbor embedding technique is applied to DSKE-TRDBRBC for keyword extraction. This in turn minimizes the time consumption for document classification. After that, the Ensemble Theil-Sen Regressive Deep Belief Reweight Boosting technique is applied for document classification. The Ensemble boosting algorithm initially constructs’ set of Theil-Sen Regressive Deep Belief neural networks to classify the input legal documents. Then the results of the Deep Belief neural network are combined to built a strong classifier by reducing the error. This aids in improving the classification accuracy. The proposed method is experimentally evaluated with various metrics such as F-measure , recall, accuracy, precision, , and computational time. The experimental results quantitatively confirm that the proposed DSKE-TRDBRBC technique achieves better accuracy with lowest computation time as compared to the conventional approaches.
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Iftitah, Widya. "PERTIMBANGAN HAKIM DALAM MENJATUHKAN PUTUSAN PIDANA DALAM TINDAK PIDANA PERZINAHAN (Studi Putusan Nomor : 217/Pid/2018/PT.Sby)." Verstek 10, no. 1 (2022): 196. http://dx.doi.org/10.20961/jv.v10i1.64056.
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<p><strong><em>ABSTRAK</em></strong><em>: Tujuan dalam penelitian hukum ini adalah untuk mengetahui dasar pertimbangan hakim dalam menjatuhkan putusan pidana pada tindak pidana perzinahan sebagaimana diatur dalam Pasal 284 ayat (1) ke-1 huruf b Kitab Undang-Undang Hukum Pidana (KUHP). Metode penelitian yang digunakan adalah penelitian normatif yang bersifat preskriptif terapan dengan menggunakan pendekatan kasus. Sumber bahan hukum meliputu bahan hukum primer dan sekunder. </em><em>Teknik analisis bahan hukum yang menggunakan teknis analisis kualitatif yang menggunakan pola berpikir deduktif yang kemudian diambil sebuah konklusi. Hasil penelitian penulisan hukum ini menunjukkan bahwa dasar-dasar pertimbangan yang digunakan oleh majelis hakim dalam menjatuhkan putusan pidana dalam kasus tindak pidana perzinahan ini telah sesuai dengan Pasal 284 ayat (1) ke-1 huruf b Kitab Undang-Undang Hukum Pidana (KUHP).</em></p><p><em>Kata Kunci : pertimbangan hakim, perzinahan, putusan.</em></p><p><strong><em>ABSTRACT</em></strong><em>: The purpose of this legal study is to known the basis for the judges’s judgment in rendering criminal judgements is committing adultery crimes as set forth in Article 284 verses (1) by-1 character b of the penal code (criminal law). The research methods used predictive and applied normative-law study using a case approach. Sources of legal material include primary and secondary legal material. </em><em>The collection of legal materials in this research used a qualitative analysis that employs a deductive method pattern that a concession was later obtained. From this legal study shown that the judge’s judgement in the decision for adultery criminal act is consist with Article 284 section (1) by-1 character b KUHP.</em></p><p><em>Keywords : judges’s judgment, adultery, verdict</em></p>