Through the MUD : A Multi-Defendant Charge Prediction Benchmark with Linked Crime Elements (2024)

Abstract: The current charge prediction datasets mostly focus on single-defendant criminal cases.However, real-world criminal cases usually involve multiple defendants whose criminal facts are intertwined. In an early attempt to fill this gap, we introduce a new benchmark that encompasses legal cases involving multiple defendants, where each defendant is labeled with a charge and four types of crime elements, i.e., Object Element , Objective Element , Subject Element , and Subjective Element . Based on the dataset, we further develop an interpretable model called EJudge that incorporates crime elements and legal rules to infer charges. We observe that predicting crime charges while providing corresponding rationales benefits the interpretable AI system. Extensive experiments show that EJudge significantly surpasses state-of-the-art methods, which verify the importance of crime elements and legal rules in multi-defendant charge prediction. The source code and dataset are available at https://anonymous.4open.science/r/MCP_1-6010 .

## introduction the charge prediction task aims to automatically recommend charges given a fact description @xcite . it has attracted substantial attention recently, leading researchers to construct high-quality datasets for its advancement, such as cail2018 @xcite and echr @xcite . commonly, existing datasets mainly support coarse-grained prediction, recommending charges for each defendant based on the whole criminal facts, without specifying relevant details. for example, fig. 1 (a ) shows a case from cail2018 @xcite ) that has only one defendant and is labeled solely with the charge, without any justification or rationale for the conviction. a single-defendant case (a) from cail2018 @xcite and a multi-defendant case (b) from our benchmark mud with crime elements. while in real-world scenarios a single case may involve multiple defendants, as shown in fig. 1 (b) with two defendants, namely, liu and wang, whose criminal facts intertwine and overlap. intuitively, addressing intricate cases with multiple defendants necessitates providing clear and compelling explanations for the criminal facts relevant to each defendant, ensuring the precision of charge predictions. unfortunately, most of the existing datasets lack fine-grained annotations of criminal facts, consequently impairing the performance of current advanced methods. as illustrated in fig. 2 (a) , several popular methods, e.g., legalbert @xcite , law-former @xcite , and roberta @xcite , show inferior performance on multiple-defendant cases (our new benchmark) compared to single-defendant cases (cail2018), with drops of 49%, 38%, and 32%, respectively. in this work, we construct a new benchmark with fine-grained annotations for multi-defendant legal cases, named mud.