摘要
通过主题建模与量化评价对我国人工智能治理政策展开系统性研究,可以为政策优化提供支撑与指引。本研究对我国从2017至2025年3月15日发布的92份国家层面人工智能治理政策中的有效数据进行预处理,使用BERTopic模型识别政策主题,计算关键词c-TF-IDF权重等3项主题指标,并结合政策内容展开治理政策重点分析。在主题建模的基础上构建PMC政策评价模型,对9项代表性政策文件展开实证量化分析。BERTopic模型共识别出13个主题,涵盖我国人工智能治理9个政策重点。PMC指数模型显示政策总体水平良好,但仍需在治理框架、治理工具以及治理主体等方面进一步优化。据此提出构建分类治理政策框架、配套政策工具组合以及推进协同治理的政策建议,以期服务我国人工智能治理实践。
By conducting a systematic study of China’s artificial intelligence governance policies through thematic modeling and quantitative evaluation,this paper provides support and guidance for policy optimization.Effective data from the 92 nationallevel artificial intelligence governance policies issued in China from 2017 to the 15th of March,2025 were preprocessed.The BERTopic model was used to identify policy themes,calculate keyword c-TF-IDF weights,and three other thematic indicators,and combined with policy content to conduct an in-depth analysis of governance policy priorities.Based on thematic modeling,the PMC policy evaluation model was constructed to conduct empirical quantitative analysis on nine representative policy documents.The BERTopic model identified 13 themes,covering nine key areas of China’s AI governance policies.The PMC index model indicates that the overall policy level is good,but further optimization is needed in terms of governance framework,governance tools,and governance entities.Based on this,policy recommendations were proposed,including the construction of a classified governance policy framework,the combination of policy tools,and the promotion of collaborative governance,to serve China’s AI governance practices.
作者
刘柄志
李勇男
Liu Bingzhi;Li Yongnan(School of National Security,People’s Public Security University of China)
出处
《信息与管理研究》
2025年第5期8-22,共15页
Journal of Information and Management
基金
中国人民公安大学基本科研业务费项目“大数据背景下国家安全情报风险态势感知模型研究”(项目编号:2024JKF01)
智慧警务与国家安全风险治理重点实验室开放课题“城市公共安全风险态势感知与防范应对研究”(项目编号:ZHKFYB2502)的研究成果之一。
