涉诉信访治理智能化处置研究  被引量:1

Intelligent Disposal of Complaint Related Petition Governance

在线阅读下载全文

作  者:杨玉晓[1] YANG Yuxiao(Center for Special Grup Rights Protection and Crime Prevention,Southwest University of Political Science and Law,Chongqing 401120,Chongqing,China)

机构地区:[1]西南政法大学特殊群体权利保护与犯罪预防中心,重庆401120

出  处:《昆明理工大学学报(社会科学版)》2021年第5期22-26,共5页Journal of Kunming University of Science and Technology(Social Sciences)

基  金:国家重点研发计划项目“多源涉诉信访智能处置技术研究”(2018YFC0831800)。

摘  要:涉诉信访法治化是涉诉信访治理的重要目标。智能化处置技术作为涉诉信访法治化的载体,重要性越来越凸显。当前,在涉诉信访案件办理中,技术水平滞后于新技术,数据智能化分析研判不足,效率低下,反映出涉诉信访中智能化处置技术开发的必要性。人工智能的发展,智慧司法的提出使得涉诉信访治理智能化处置具有推广的可行性。我们只有通过打造数据共享的APP提高涉诉信访处置效率,开发去中心化的APP建立"数字信任",推广加密化的APP强化保密性,才能把智能化技术融入涉诉信访,打造"法技融合"的智能化载体。The legalization of complaint letters and visits is an important goal of complaint letters and visits governance. As the carrier of legalization of complaint letters and visits, intelligent disposal technology is becoming increasingly important. At present, in the handling of litigation related petition cases, the intelligent level lags behind the new technology, and the data analysis and judgment are insufficient, which reflects the necessity of the development of intelligent disposal technology of litigation related petition letters. With the development of artificial intelligence and the proposal of intelligent justice, it is feasible to promote the intelligent disposal of litigation related petition governance. Only by building a data sharing app to improve the handling efficiency of litigation related letters and visits, developing decentralized app, establishing "digital trust", promoting encrypted apps and strengthening confidentiality, can we integrate intelligent technology into litigation related letters and visits and create an intelligent carrier of "integration of law and technology".

关 键 词:涉诉信访 智能化处置技术 法技融合 智能化载体 法治化 

分 类 号:D925[政治法律—诉讼法学]

 

参考文献:

正在载入数据...

 

二级参考文献:

正在载入数据...

 

耦合文献:

正在载入数据...

 

引证文献:

正在载入数据...

 

二级引证文献:

正在载入数据...

 

同被引文献:

正在载入数据...

 

相关期刊文献:

正在载入数据...

相关的主题
相关的作者对象
相关的机构对象