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Journal of library and information science in agriculture ›› 2025, Vol. 37 ›› Issue (11): 4-29.doi: 10.13998/j.cnki.issn1002-1248.25-0729

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Constructing a Cross-border Data Governance Paradigm in the International Cooperation Mechanism of Artificial Intelligence

ZHAO Hui1(), CHEN Jinghao2(), GUO Sha3(), LI Zhixing4(), YAN Longfei5()   

  1. 1. School of Economics, Guangxi University, Nanning 530004
    2. School of Public Administration, Guangxi University, Nanning 530004
    3. Shenzhen Xingsheng Digital Technology Co. , Ltd. , Shenzhen 518067
    4. Far East Credit Rating Co. , Ltd. , Shanghai 200092
    5. Shiyuan Digital Technology Co. , Ltd. , Guiyang 550081
  • Online:2025-11-05 Published:2025-12-29
  • Contact: ZHAO Hui, CHEN Jinghao, GUO Sha, LI Zhixing, YAN Longfei

Abstract:

In the digital economy era, the efficient, secure, and compliant circulation of cross-border data flow has become a key issue for the coordination of global industrial chains and the deepening of regional cooperation. It is a driving force for the high-quality development of the global digital economy. Currently, cross-border data flow is confronted with multiple challenges, including the interweaving of driving forces and contradictions, inadequate adaptation between mechanisms and technologies, and poor connection between compliance requirements and practical implementation. There is an urgent need to formulate systematic solutions from both theoretical and practical perspectives. To this end, this journal has invited five experts from universities and enterprises to organize a roundtable discussion on the complete logical chain of "the underlying logic, mechanism construction, trend prediction, compliance governance, and scenario-based implementation of cross-border data flow". The key viewpoints are as follows: 1) Dynamic Mechanism and Governance Logic of Cross-border Data Flow: Cross-border data flow is jointly driven by three major forces: economic interests, technological innovation, and international cooperation. Meanwhile, it faces core contradictions including the trade-off between sovereign security and flow efficiency, fragmentation of rules and institutional coordination, and technological balance and the digital divide. It is necessary to establish a governance philosophy of "dynamic balance" and build a multilateral co-governance system through three types of tools-algorithm-based supervision, technology empowerment, and institutional experimentation-to promote the shift from "fragmented rule-based games" to "systematic coordination". 2) Construction of a Collaborative Mechanism for Cross-border Data Flow: The mechanism for cross-border data flow needs to break through the limitations of a single dimension and form a multi-dimensional collaborative system integrating "policy, technology, and industry". At the policy level, regulatory sandbox pilots, standard mutual recognition, and compliance infrastructure sharing are adopted to address regulatory barriers. At the technical level, scenario-specific needs are met based on a maturity gradient, and the integrated innovation of "technology + management" is promoted. At the industry level, the self-regulatory role of professional fields such as library and information science (LIS) is leveraged to compensate for the rigidity of policies and build a closed-loop governance structure. 3) Trend Evolution and Risk Resilience of Cross-border Data Flow: In the next 3 to 5 years, cross-border data flow will exhibit characteristics of structural growth and domain differentiation. Smart manufacturing and digital trade will drive growth on a large scale, while smart healthcare and modern agriculture will emerge as core sectors. It is imperative to address bottlenecks in infrastructure upgrading and the impact of "black swan" events, establish a risk resilience system from technical, governance and strategic dimensions, and promote service model innovation in LIS as well as advance layout in the agricultural sector. 4) Compliance Governance and China's Path for Cross-border Data Flow: China has established a hierarchical and classified governance framework centered on three fundamental laws, and explored practical paths through institutional innovations such as the negative list system in free trade pilot zones. To tackle challenges including discrepancies in legal compliance requirements, technical barriers, and the complexity of regulatory coordination, it is necessary to strengthen legal synergy and rule mutual recognition, advance infrastructure construction and technological innovation, and improve the compliance service support system, thereby forming a China-specific path that balances security and controllability with high efficiency and convenience. 5) Practice of Cross-border Data Circulation and Credit Product Mutual Recognition: Cross-border data circulation lays a core foundation for the cross-border mutual recognition of credit products, which holds significant strategic value for promoting the facilitation of international trade and supporting the international development of enterprises. Currently, it faces challenges such as data security compliance, standard discrepancies, and high technical costs. To advance the implementation of cross-border mutual recognition of credit products, efforts should be made to improve the legal and regulatory framework and standard system, strengthen the construction of technical infrastructure, deepen international cooperation and mutual recognition mechanisms, and cultivate international credit service institutions.

Key words: trusted data space, data element valorization, AI-empowered compliance, cross-border data trust, data justice, technology inclusiveness, regional collaborative governance

CLC Number: 

  • G250.7

Table 1

“Black Swan" events and data flow resilience strategies"

事件类型 具体表现 韧性提升路径
技术突变 量子计算技术突破现有非对称加密体系,导致跨境数据传输中的加密机制失效,敏感数据面临泄露风险 技术层面:优先在金融、医疗等高敏感数据跨境场景部署抗量子加密算法,建立“加密算法动态更新机制”;治理层面:推动全球抗量子技术标准协同,避免“技术碎片化”;战略层面:储备多套加密方案,应对技术突变风险
地缘政治剧变 主要经济体形成排他性“数据联盟”,实施数据“硬性脱钩”,限制非联盟国家数据流动 治理层面:建立弹性合规框架,针对不同联盟规则制定适配方案,同时推动“小多边”合作(如中国-东盟、中国-中东欧),分散单一联盟依赖风险;战略层面:布局自主可控的技术栈(如国产数据库、隐私计算工具),降低技术依赖
重大数据灾难 关键基础设施(如海底光缆、跨境数据中心)遭遇网络攻击或自然灾害(如地震、海啸),导致数据传输中断 技术层面:建立数据“多地备份”机制(如在不同区域部署备份节点),采用“多路径传输”技术,避免单一基础设施依赖;治理层面:联合相关国家制定跨境数据灾难应急预案,明确各国救援责任与协作流程;战略层面:推动基础设施多元化,降低对单一光缆、数据中心的依赖

Table 1

Impacts of cross-border mutual recognition of credit products in key fields"

应用领域 当前挑战 互认后的效益
跨境融资 境外金融机构难以获取境内企业信用信息,授信审批难 提高融资可得性,降低融资成本,扩大融资规模
国际贸易 交易双方信用信息不透明,增加交易风险和成本 减少信息不对称,提高交易效率,降低交易风险
投资合作 投资方难以评估目标企业真实信用状况 提供决策依据,促进投资便利化,保护投资者权益
供应链管理 跨境供应链信用信息割裂,难以全面评估风险 实现供应链全链条信用可视化管理,提高供应链韧性

Table 2

Main application scenarios of AI technology in credit evaluation"

应用场景 技术特点 数据需求
信用评分 利用机器学习算法分析多维度数据,生成信用分数 需要大量历史数据和行为数据进行模型训练
风险预警 通过实时监测和分析数据,发现潜在信用风险 需要实时数据流和高质量的特征工程
反欺诈 运用图神经网络等技术识别复杂欺诈模式 需要多源异构数据和强大的计算能力
供应链金融 分析供应链数据,评估供应链信用风险 需要完整的供应链数据和交易数据
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