信息安全研究 ›› 2026, Vol. 12 ›› Issue (2): 142-.

• 数字社会的安全、隐私与治理专题 • 上一篇    下一篇

欧盟法视角下正当利益规则的中国镜鉴

张泽辰   

  1. (澳门大学法学院中国澳门999078)
  • 出版日期:2026-02-07 发布日期:2026-01-28
  • 通讯作者: 张泽辰 博士研究生.主要研究方向为隐私与数据安全治理. yc47259@um.edu.mo
  • 作者简介:张泽辰 博士研究生.主要研究方向为隐私与数据安全治理. yc47259@um.edu.mo

China’s Mirror and Insights for the Legitimate Interest Rule from  the EU Law Perspective

Zhang Zechen   

  1. (Faculty of Law, University of Macau, Macao, China 999078)
  • Online:2026-02-07 Published:2026-01-28

摘要: 生成式人工智能的快速发展对传统知情同意规则构成严峻挑战.欧盟通过《通用数据保护条例》创设的“正当利益规则”,以开放性结构和动态平衡机制,有效调和了数据保护与技术创新的矛盾;而《中华人民共和国个人信息保护法》与欧盟立法在个人数据合法处理的规范构成要素上存在差异,导致知情同意规则难以满足生成式人工智能场景下海量数据处理需求.欧盟经验植根于其权利保护与风险管理并重的治理传统,以及统一市场导向的经济逻辑;我国侧重采取风险规制策略,形成了“强保护、弱流通”的规范路径.针对生成式人工智能技术特性,我国应构建本土化的正当利益规则:限定于商业化场景,引入“利益测试—必要性测试—平衡测试”3阶分析框架,配套风险缓释措施与问责机制.这一制度创新既可突破知情同意规则的适用困境,又能通过个案裁量实现数据主体权益、商业利益与公共价值的动态平衡,为破解人工智能时代的数据治理困境提供兼具理论价值与实践可行性的解决方案.

关键词: 生成式人工智能, 知情同意, 正当利益规则, 数据治理, 中欧比较

Abstract: The rapid development of generative artificial intelligence (GAI) poses significant challenges to traditional informed consent rules. The European Union (EU) addresses this tension through the “legitimate interest rule” established under the General Data Protection Regulation. The EU effectively reconciles data protection with technological innovation by adopting an openstructured framework and dynamic balancing mechanisms. In contrast, China’s Personal Information Protection Law diverges from the EU counterpart in terms of the data processing lawfulness, rendering informed consent rules challenging to meet the demands of largescale data processing in the context of GAI. The EU’s approach is rooted in its governance doctrine that harmonizes rights protection with risk management, alongside an economic logic prioritizing a unified market. China adopts a riskbased regulatory strategy and has developed a “strong protection, weak circulation” regulatory model. To address the technical complexities of GAI, China should construct a localized legitimate interest rule which is confined to applications in commercial scenarios. This framework would incorporate a threetiered analysis—interest test, necessity test, and balance test—supported by risk mitigation measures and accountability mechanisms. Such institutional innovation would overcome the consent application dilemma while enabling adjudication to dynamically balance data subjects’ rights, commercial interests, and public values casebycase. This solution offers both a theoretical framework and practical feasibility for optimizing data governance in the AI era.

Key words: generative artificial intelligence, informed consent, legitimate interest rule, data governance, ChinaEU comparative analysis

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