{"type":"ScholarlyArticle","title":"LEEC for Judicial Fairness: A Legal Element Extraction Dataset with Extensive Extra-Legal Labels","authors":[{"name":"Zongyue Xue","slug":"zongyue-xue","given":"Zongyue","family":"Xue","affiliation":"School of Law , Tsinghua University; Yale Law School {zongyuexue ,"},{"name":"Huanghai Liu","slug":"huanghai-liu","given":"Huanghai","family":"Liu","affiliation":"School of Law , Tsinghua University"},{"name":"Yiran Hu","slug":"yiran-hu","given":"Yiran","family":"Hu","affiliation":"School of Law , Tsinghua University"},{"name":"Yuliang Qian","slug":"yuliang-qian","given":"Yuliang","family":"Qian","affiliation":"School of Law , Tsinghua University"},{"name":"Yajing Wang","slug":"yajing-wang","given":"Yajing","family":"Wang","affiliation":"School of Law , Tsinghua University"},{"name":"Kangle Kong","slug":"kangle-kong","given":"Kangle","family":"Kong","affiliation":"School of Law , Tsinghua University"},{"name":"Chenlu Wang","slug":"chenlu-wang","given":"Chenlu","family":"Wang","affiliation":"School of Law , Tsinghua University"},{"name":"Yun Liu","slug":"yun-liu","given":"Yun","family":"Liu","affiliation":"School of Law , Tsinghua University"},{"name":"Weixing Shen","slug":"weixing-shen","given":"Weixing","family":"Shen","affiliation":"School of Law , Tsinghua University"}],"abstract":"An extensive label system is pivotal to facilitate judicial fairness and social justice. Prior empirical research and our interview with legal professionals underscore the importance of extra-legal factors in criminal trials. To help identify sentencing biases and facilitate downstream applications, we introduce the Legal Element ExtraCtion (LEEC) dataset comprising 15,919 judicial documents and 155 labels. This dataset was constructed through two main steps: First, designing the label system by legal experts based on prior empirical research which identified critical factors driving and processes generating sentencing outcomes in criminal cases; Second, employing legal knowledge to annotate judicial documents according to the label system and annotation guideline. LEEC represents the most extensive and domain-specific legal element extraction dataset for the Chinese legal system. Our experiments reveal that despite certain capabilities, both Document Event Extraction (DEE) models and Large Language Models(LLMs) face significant restrictions in legal element extraction tasks. Finally, our empirical analysis based on LEEC provides evidence for judicial unfairness in Chinese criminal sentencing and confirms the applicability of LEEC for future empirical research and other downstream applications. LEEC and related resources are available on https://github.com/THUlawtech/LEEC.","doi":"10.24963/ijcai.2024/833","url":"https://ijcai.org/proceedings/2024/833","pdf":"https://ijcai.org/proceedings/2024/0833.pdf","markdown":"https://ijcai.org/proceedings/2024/833.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/833","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"AI for Good","track":"AI for Good","number":833,"pages":{"first":7527,"last":7535},"keywords":["Multidisciplinary Topics and Applications: General","Natural Language Processing: General"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2023] Andong Chen, Feng Yao, Xinyan Zhao, Yating Zhang, Changlong Sun, Yun Liu, and Weixing Shen. 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