生成式人工智能数据风险的差异化监管路径研究——基于“公共数据两级市场”模型

A Study on Differentiated Regulatory Pathways for Data Risks in Generative Artificial Intelligence—Grounded in the “Two-Tier Public Data Market” Model

摘要: 生成式人工智能技术以其强大的内容生成能力在各类数据利用场景中快速渗透，并深刻改变着数据要素的配置逻辑与治理机制。在释放巨大生产力的同时，其数据源的不透明性、生成过程的不可控性以及内容输出的不可靠性，也引发了多层次的数据安全风险。立足生成式人工智能的发展现状，系统梳理其数据风险的多元缘起，聚焦数据利用层的数据源片面性、模型运算层的算法隐患、内容产出层的信息失真，深入分析了信息不公、虚假信息生成与市场信息污染等风险类型。溯源分析表明，风险的生成与扩散贯穿数据采集、模型训练与内容输出全流程，公共数据的公共属性与安全敏感性决定了政府行政部门必须承担主导监管责任。在此基础上，进一步提出构建“公共数据两级市场 + 数据差异化监管”模型，从一级市场源头管控与二级市场全流程合规等维度出发，形成适应性强、操作性高、前瞻性足的行政监管体系，以期实现生成式人工智能数据治理由被动响应向主动治理、由单一规制向差异化协同的范式转型，从而确保数据利用合法合规与技术创新的动态平衡，推动人工智能行业可持续发展。

Abstract: Generative artificial intelligence (GenAI) technology, with its powerful content-generation capabilities, has rapidly penetrated various data-utilization scenarios and profoundly reshaped the allocation logic and governance mechanisms of data factors. While unleashing substantial productivity, its opaque data sources, uncontrollable generation processes, and unreliable output content have also triggered multi-layered data security risks. Based on the current development of generative artificial intelligence, this paper systematically examines the diverse origins of its data risks, focusing on the one-sidedness of data sources at the data-utilization layer, the algorithmic hazards at the model-computation layer, and information distortion at the content-output layer, and provides an in-depth analysis of risk types such as information inequality, the generation of false information, and market information pollution. A source-tracing analysis reveals that the generation and diffusion of risks run through the entire process of data collection, model training, and content output, and that the public attributes and security sensitivity of public data determine that government administrative departments must assume the primary regulatory responsibility. On this basis, the paper further proposes the construction of a “two-tier public data market + differentiated data regulation” model, which, from the dimensions of source control in the primary market and full-process compliance in the secondary market, forms an administrative regulatory system that is highly adaptive, highly operable, and forward-looking. This is intended to achieve a paradigm shift in GenAI data governance from passive response to proactive governance and from single-form regulation to differentiated and coordinated regulation, thereby ensuring a dynamic balance between lawful and compliant data utilization and technological innovation, and promoting the sustainable development of the artificial intelligence industry.