大数据技术背景下个人信息法律保护的困境与完善

The Dilemmas and Improvements in the Legal Protection of Personal Information in the Context of Big Data Technology

摘要: 随着大数据技术的广泛应用，个人信息在收集、处理与流转中面临过度收集、算法黑箱与维权举证难等法律困境。尽管我国已出台《个人信息保护法》等法律法规，但“告知–同意”原则形式化、自动化决策不透明、用户维权成本高等问题依然突出。本文从法理学与经济学双重视角特征切入，分析当前个人信息保护面临的现实挑战与立法不足，指出信息收集环节的强制授权、处理环节的算法歧视以及救济环节的举证困难是核心痛点。为此，提出相应的完善路径：重构“告知–同意”规则，引入分层同意机制；强化算法治理与平台问责，保障用户拒绝自动化决策的权利；优化司法救济，实行举证责任缓和并推广个人信息保护公益诉讼。以期在促进数据要素合理利用的同时，实现对个人信息的有效法律保护。

Abstract: With the widespread adoption of big data technologies, personal information faces various legal challenges during its collection, processing, and circulation—such as excessive data collection, the opacity of algorithmic systems, and the difficulty of providing evidence to safeguard users’ rights. Although China has enacted laws and regulations—including the Personal Information Protection Law—significant issues remain, such as the formalization of the “informed consent” principle, the lack of transparency in automated decision-making, and the high cost for users to assert their rights. This paper adopts a dual perspective rooted in both jurisprudence and economics to analyze the current practical challenges and legislative gaps in personal information protection, identifying three core pain points: mandatory authorization during data collection, algorithmic discrimination during data processing, and the evidentiary burden in remedies mechanisms. To address these issues, the paper proposes corresponding improvement pathways: restructuring the “informed consent” rule by introducing a tiered consent mechanism; strengthening algorithmic governance and platform accountability to safeguard users’ right to refuse automated decision-making; and optimizing judicial remedies by implementing a reduced burden-of-proof approach and promoting public interest litigation for personal information protection. These measures aim to facilitate the rational utilization of data as a production factor while ensuring effective legal protection of personal information.