自动化决策算法中的个人信息侵权因果关系认定  

Addressing the challenges of establishing causation in personal information tort under automated decision-making algorithms

作　　者：姚朱沁 Yao Zhuqin(School of Law,East China University of Politics,Science and Law,Shanghai 201620,China)

机构地区：[1]华东政法大学法律学院,上海201620

出　　处：《网络安全与数据治理》2025年第3期96-102,共7页CYBER SECURITY AND DATA GOVERNANCE

摘　　要：自动化决策算法与传统侵权法体系之间存在适用上的矛盾,亟须通过因果关系的认定予以化解。对此,应在坚持传统的因果关系构成要件认定标准基础上,结合比例因果关系理论,对各因素在损害结果中的贡献进行量化分析。针对单一信息处理者场景,可以在疑难案件中引入“合理盖然性”标准降低受害人举证难度;对于多主体参与的情形,通过类推适用共同危险行为制度实现举证责任倒置,但需以整体行为与损害的因果关系证成为前提。此法律框架有助于在多方数据处理和信息流转背景下,更有效地解决技术复杂性带来的法律挑战,推动信息保护与技术发展的平衡。The interaction between automated decision-making algorithms and traditional tort law presents inherent conflicts,which should be resolved by the identification of causality.To address this,it is necessary to maintain the traditional criteria for establishing causality while integrating the theory of proportional causality to quantify the contribution of each factor to the resulting harm.In cases involving a single data processor,the"reasonable probability"standard can be introduced in complex cases to reduce the burden of proof for the victim.In scenarios involving multiple participants,the doctrine of joint dangerous activities can be applied by analogy to reverse the burden of proof,provided that the victim can demonstrate the causal relationship between the collective actions and the harm.This legal framework helps to more effectively address the legal challenges arising from the technical complexity of multi-party data processing and information flow,promoting a balance between information protection and technological development.

关 键 词：因果关系 算法侵权 相当性 证明责任分配 共同危险行为 

分 类 号：D923[政治法律—民商法学]