人工智能算法伦理风险的适应性治理研究——基于浙江实践与欧美经验的整合框架

Adaptive Governance of Ethical Risks in Artificial Intelligence Algorithms—An Integrated Framework Based on Zhejiang Practice and European and American Experience

摘要: 人工智能算法伦理风险正成为制约技术赋能实体经济与包容性增长的关键瓶颈。本文以浙江省数字经济实践为研究场域，聚焦自动驾驶、AIGC等高频应用场景中的伦理治理挑战。基于“技术–社会–主体”三维分析框架，研究系统揭示了算法伦理风险的生成逻辑与多维呈现。研究发现，浙江在治理实践中虽展现出多元协同与敏捷治理特征，但仍面临治理科技鸿沟、标准缺失与协同梗阻等“系统性迟滞”。通过对欧盟刚性规制与美国柔性治理范式的比较与反思，本文构建了以监管沙盒为核心引擎的“基于风险的适应性治理”框架。该框架依托规制、技术与市场三大支柱，通过“风险识别–动态监管–效果评估–规则迭代”的闭环运行机制，实现技术创新与伦理规范的动态平衡。研究为解构算法伦理风险提供了系统理论视角，也为完善人工智能治理体系提供了兼具前瞻性与操作性的路径参考。

Abstract: Ethical risks in artificial intelligence algorithms are becoming a critical bottleneck that hinders technology from empowering the real economy and fostering inclusive growth. This paper takes the digital economy practices of Zhejiang Province as the research context, focusing on ethical governance challenges in high-frequency application scenarios such as autonomous driving and AI-generated content (AIGC). Based on a “technology-society-agent” three-dimensional analytical framework, the study systematically reveals the generative logic and multidimensional manifestations of algorithmic ethical risks. The research finds that although Zhejiang’s governance practices exhibit characteristics of multi-stakeholder collaboration and agile governance, they still face issues of “systemic lag”, such as gaps in governance technology, lack of standards, and coordination obstacles. Through a comparative analysis and reflection on the rigid regulatory paradigm of the European Union and the flexible governance approach of the United States, this paper constructs a “risk-based adaptive governance” framework with the regulatory sandbox as its core engine. Supported by three pillars—regulation, technology, and market—this framework achieves a dynamic balance between technological innovation and ethical norms through a closed-loop operational mechanism of “risk identification-dynamic supervision-effect evaluation-rule iteration”. The study provides a systematic theoretical perspective for deconstructing algorithmic ethical risks and offers a forward-looking and actionable reference for improving the artificial intelligence governance system.