1672-8505

CN 51-1675/C

人工智能国际治理转型:风险分级的“核心硬法—周边软法”同心圆治理模型

Transforming International AI Governance through a Risk-tiered Concentric-circle Model of Core Hard Law and Peripheral Soft Law

  • 摘要: 由于人工智能技术的高速迭代性以及复杂性,主权合意驱动下的传统国际法线性演进路径在应对人工智能国际治理议题时产生了一定的局限性,并在实践中具象化为人工智能国际治理体系中硬法碎片化与软法效力赤字、技术优势竞争下的立法合意僵局、多元行为体的治理目标冲突与角色失序等现实困境。面对现实困境,人工智能国际治理应当摒弃软硬法二元对立的静态线性立法思维,转向构建功能互补、协同进化的“动态适应性规制”框架。具体而言,可以建构基于风险分级的“核心硬法—周边软法”同心圆治理模型,使处于核心的硬法聚焦于划定基本人权与公共安全的不可逾越红线,而外围的软法集群则通过敏捷试错为特定技术场景提供灵活指引,并在两者之间建立动态反馈衔接机制。同时,进一步构建依托现有国际体系的常态化协调机制、多方参与的动态审议与适应性评估机制、开放的人工智能治理信息共享与报告系统、贯穿始终的公众参与及人工智能治理问责保障等辅助协调组织制度,以确保该治理模型具备随技术风险演进而自我更新的生命力,最终实现安全底线与创新活力的动态平衡。

     

    Abstract: Given the rapid evolution and growing complexity of AI technologies, the traditional evolutionary trajectory of international law, grounded in sovereign consensus, has become increasingly ill-equipped to address the challenges of international AI governance. In practice, this inadequacy has manifested itself in several major challenges, including the fragmentation of hard law, the limited effectiveness of soft law, deadlocks in international rule-making driven by competition for technological advantage, and conflicts in governance objectives together with fragmented institutional roles among multiple stakeholders. In response, international AI governance should move beyond the static, binary distinction between hard and soft laws and embrace a dynamic and adaptive regulatory framework based on functional complementarity and co-evolution. Specifically, this article proposes a risk-tiered concentric-circle governance model in which core hard law establishes non-negotiable safeguards for fundamental human rights and public safety, while a network of peripheral soft-law instruments provides flexible guidance for specific technological contexts through agile experimentation. The two layers are integrated through a dynamic feedback mechanism that enables continuous mutual adjustment. To support this model, complementary institutional arrangements should be established, including an institutionalized coordination mechanism within the existing international system, a multi-stakeholder mechanism for ongoing review and adaptive assessment, an open information-sharing and reporting system for AI governance, and sustained institutional safeguards for public participation and accountability. Together, these mechanisms would enable the governance framework to adapt continuously to evolving technological risks while maintaining an appropriate balance between safety and innovation.

     

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