大数据法律监督模型的实践面向与优化路径

Practice Orientation and Optimization Path of Big Data Legal Supervision Model

大数据分析蕴含的相关关系具有丰富性,催生了检察业务的融合式监督方式,获得的类案监督线索促进办案模式向类案监督、系统治理转型,这相较于个案办理更易发现深层次的制度漏洞或司法工作人员的违法犯罪等行为。实践中,可以将大数据法律监督模型分为犯罪画像型、数据挖掘型、数据比对型、规则耦合型和规则循环型,各类型均具有较为明显的监督场景。值得注意的是,模型构建及运用方面,存在建模单兵化、监督项目化、优势倾斜化等问题。大数据法律监督模型的优化,应当注意限度与实用性能,结合模型的特性灵活运用到监督场景中,避免选择的盲目性;树立正反思维,注重参与社会治理的同时不放松对司法权的监督;注重“普及基础上的提高”,多措并举激发检察官对大数据法律监督模式的应用热情。

The correlational relationship contained in big data analysis is rich,giving rise to an integrated supervision method for procuratorial business.The obtained supervision clues for similar cases promote the transformation of case-handling models to supervision of similar cases and systematic governance.Compared with handling individual cases,this is more likely to discover deep-seated institutional loopholes or illegal and criminal acts of judicial staff.In practice,big data legal supervision models can be divided into different types,that is,crime portrait,data mining,data comparison,rule coupling,and rule circulation.Each type has relatively obvious supervision scenarios.It is worth noting that in terms of model construction and application,there are problems such as individualized modeling,project-based supervision,and tilted advantages.For the optimization of big data legal supervision,attention should be paid to limits and practicability.Combined with the characteristics of the model,it should be flexibly applied to supervision scenarios to avoid blind selection.Establish positive and negative thinking,pay attention to participating in social governance while not relaxing supervision over judicial power.Focus on“improvement on the basis of popularization”and take multiple measures to stimulate prosecutors’enthusiasm for applying the big data legal supervision model.