从结构与管理观点看法教义学的未来——通过实用教义学的AI化开拓理论教义学的新境界
The Future of the Legal Dogmatics in the Perspective of Structure and Management:New Frontier of Theoretical Dogmatics with Practical Dogmatics in Al's Hand
苏永钦 1周翔 2章程2
作者信息
- 1. 台湾政治大学
- 2. 浙江大学光华法学院
- 折叠
摘要
不同于英美法系法教义学的事后—个案研究本质,大陆法系的事前—体系研究固有高度可预测性的维稳功能,但不仅在满足个案特殊情况期待的公平上较为不足,更因法律人投入资源之巨,使得原可保留做理论研究的人力受到挤压,以致形成长期失衡难以逆转的结构.对于没有法学传统,其法治的建构只能高度依赖系统性法制继受的国家,包括中国,其整体教义学研究的投入和产出可说同样偏弱,根本还谈不到实用与理论间的失衡.因此生成式人工智能的新发展,尤其源于其与法教义学近似的学习、生成方式,而有快速消化海量数据的能力,果能做好阶段性规划,包括法律教育与司法体制的重新部署,应可根本翻转大陆法系国家法教义学的失衡结构,或彻底改善其先天不足的处境,大陆法系背后的法治国理念,既都高度倚赖法教义学的支撑,国家应可适度扮演规划监督者的角色.
Abstract
The ex ante-in system approach of the legal dogmatics in the Civil Law countries,unlike the ex post-per case approach in the Common Law countries,bears stabilizing function with its high degree of foreseeability,while being relatively weak in fulfilling the expectation of equity for hard cases.Besides,by exhausting research resources more and more in the practical dogmatics,the manpower left to the theoretical dogmatics becomes limited,leading gradually to an unreversibly unbalanced research structure.The picture is dimmer for those countries which have adopted their legal systems from European countries,including China,because the strong demand for legal dogmen is not met either in the practical or in the theoretical dogmatics.The authors believe,however,that the generative artificial intelligence,which has similar character to the dogmatical thinking and the capability to generate knowledge based on the immense information,could be trained to supply practical dogmen in need,and thus maintaining a more efficient dogmatic structure.In this process the state might be expected in a Civil Law country to play a more active role,since in its concept of rule by law it should be the mission of a state to see that legal dogmatics well functions.
关键词
法教义学/生成式人工智能/实用教义学/理论教义学/法务科技Key words
Legal Dogmatics/Generative Artificial Intelligence(Generative AI)/Practical Dogmatics/Theoretical Dogmatics/Legal Tech引用本文复制引用
出版年
2024
NETL
NSTL
万方数据