As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework...As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.展开更多
The objective phenomenon of reliance on confessions is the source of many problems in the practice of criminal justice in China. Although successive amendments to the law have endeavored to improve the handling of con...The objective phenomenon of reliance on confessions is the source of many problems in the practice of criminal justice in China. Although successive amendments to the law have endeavored to improve the handling of confessions, they have been unable to resolve the dilemma of a rigid management mode, incompatible management strategies, and conflicting attitudes towards management. This paper has made a multi-dimensional reinterpretation of it from the judges＇ perspective, including functional analysis, cultural interpretation and immediate considerations. It reveals that behind its functions of offering moral comfort or allowing evasion of responsibility, confession, as a medium of physical and intellectual management, has the more important function of supplementing and improving the legitimacy of criminal verdicts. The remolding of the legitimacy of criminal verdicts with the coordinated improvement of the management of confessions will change the mode of association between those who currently govern confession and those they govern and will clash with the current closed mode of criminal justice management, thereby promoting a transformation in the governance of criminal justice.展开更多
基金funded by the Deanship of Scientific Research at Jouf University under Grant number DSR-2022-RG-0101。
文摘As legal cases grow in complexity and volume worldwide,integrating machine learning and artificial intelligence into judicial systems has become a pivotal research focus.This study introduces a comprehensive framework for verdict recommendation that synergizes rule-based methods with deep learning techniques specifically tailored to the legal domain.The proposed framework comprises three core modules:legal feature extraction,semantic similarity assessment,and verdict recommendation.For legal feature extraction,a rule-based approach leverages Black’s Law Dictionary and WordNet Synsets to construct feature vectors from judicial texts.Semantic similarity between cases is evaluated using a hybrid method that combines rule-based logic with an LSTM model,analyzing the feature vectors of query cases against a legal knowledge base.Verdicts are then recommended through a rule-based retrieval system,enhanced by predefined legal statutes and regulations.By merging rule-based methodologies with deep learning,this framework addresses the interpretability challenges often associated with contemporary AImodels,thereby enhancing both transparency and generalizability across diverse legal contexts.The system was rigorously tested using a legal corpus of 43,000 case laws across six categories:Criminal,Revenue,Service,Corporate,Constitutional,and Civil law,ensuring its adaptability across a wide range of judicial scenarios.Performance evaluation showed that the feature extraction module achieved an average accuracy of 91.6%with an F-Score of 95%.The semantic similarity module,tested using Manhattan,Euclidean,and Cosine distance metrics,achieved 88%accuracy and a 93%F-Score for short queries(Manhattan),89%accuracy and a 93.7%F-Score for medium-length queries(Euclidean),and 87%accuracy with a 92.5%F-Score for longer queries(Cosine).The verdict recommendation module outperformed existing methods,achieving 90%accuracy and a 93.75%F-Score.This study highlights the potential of hybrid AI frameworks to improve judicial decision-making and streamline legal processes,offering a robust,interpretable,and adaptable solution for the evolving demands of modern legal systems.
文摘The objective phenomenon of reliance on confessions is the source of many problems in the practice of criminal justice in China. Although successive amendments to the law have endeavored to improve the handling of confessions, they have been unable to resolve the dilemma of a rigid management mode, incompatible management strategies, and conflicting attitudes towards management. This paper has made a multi-dimensional reinterpretation of it from the judges＇ perspective, including functional analysis, cultural interpretation and immediate considerations. It reveals that behind its functions of offering moral comfort or allowing evasion of responsibility, confession, as a medium of physical and intellectual management, has the more important function of supplementing and improving the legitimacy of criminal verdicts. The remolding of the legitimacy of criminal verdicts with the coordinated improvement of the management of confessions will change the mode of association between those who currently govern confession and those they govern and will clash with the current closed mode of criminal justice management, thereby promoting a transformation in the governance of criminal justice.
