AI时代数字乡村建设中统计数据的现实梗阻与纾困路径

Practical Obstacles to Statistical Data in the Development of Digital Villages in the Age of AI, and Pathways to Overcoming Them

摘要: 农业农村现代化关系着中国式现代化的全局，数字乡村建设是推动乡村全面振兴的重要支撑。在生成式人工智能(Generative AI, GAI)快速发展的背景下，AI技术快速渗透乡村生产、治理和公共服务各个领域场景，进一步凸显了统计数据在政策制定、过程监测和成效评估中的基础作用。然而，当前数字乡村统计数据在采集、质量控制、分析应用与治理机制等方面面临诸多现实挑战，制约了数字乡村建设的精细化、科学化与可持续推进。本文紧扣“十五五”时期农业农村现代化规划要求，从统计学理论与数据治理视角出发，系统剖析AI时代数字乡村统计数据在基础设施、制度设计、技术应用范式及多元治理结构层面的深层梗阻。研究发现，相关困境并非单纯源于技术层面的不足，更深层次的矛盾体现为：数据生成过程不透明、统计制度激励不相容、基层数据压力累积，以及AI应用“重预测、轻推断”的导向偏差。因此，本文提出了以数据生成过程为核心，构建协同共享数据生态；优化激励相容的统计制度设计；坚持“AI赋能统计推断”而非“AI替代统计工作”的应用范式；创新多主体协同的统计数据的治理格局。进而为提升数字乡村统计数据质量，支撑数字乡村高质量发展提供理论参考与实践依据。

Abstract: The modernization of agriculture and rural areas is integral to the overall picture of China’s modernization, and the development of digital villages serves as a crucial pillar for promoting comprehensive rural revitalization. Against the backdrop of the rapid development of generative artificial intelligence (Generative AI, GAI), AI technology is rapidly permeating various aspects of rural production, governance, and public services, further highlighting the foundational role of statistical data in policy formulation, process monitoring, and effectiveness evaluation. However, current statistical data on digital rural development faces numerous practical challenges in data collection, quality control, analysis and application, and governance mechanisms, which hinder the refined, scientific, and sustainable advancement of digital rural development. Aligning closely with the requirements of the “15th Five-Year Plan” for the modernization of agriculture and rural areas, this paper systematically analyzes—from the perspectives of statistical theory and data governance—the deep-seated obstacles to statistical data on digital rural development in the AI era, focusing on infrastructure, institutional design, technological application paradigms, and multi-stakeholder governance structures. The study finds that these challenges do not stem solely from technical shortcomings; rather, deeper-rooted contradictions manifest as: opacity in the data generation process, incompatible incentives within the statistical system, mounting data collection pressures at the grassroots level, and a bias in AI applications that “prioritizes prediction over inference.” Therefore, this paper proposes building a collaborative and shared data ecosystem centered on the data generation process; optimizing the design of statistical systems to ensure incentive compatibility; adhering to an application paradigm of “AI empowering statistical inference” rather than “AI replacing statistical work”; and innovating a multi-stakeholder collaborative governance framework for statistical data, which will provide theoretical reference and practical basis for improving the quality of digital statistical data in rural and supporting the high-quality development of digital rural areas.