Data Risks and Regulation Approaches of Generative Artificial Intelligence
As a pivotal technology driving the new round of technological revolution and industrial transformation,generative artificial intelligence(AI)will lead artificial intelligence technology to a singularity and trigger a comprehensive explosion of AI applications.However,in the new era of artificial intelligence dominated by generative AI,its data-driven nature and inherent technical logic pose a series of fundamental and systemic challenges in data risk governance across various levels,including data input,storage,model training,and content output.At the data input level,the risks are encountered in terms of quality and accuracy,bias and representativeness,privacy and copyright protection,and contamination and manipulation of service corpora At the storage level,the risks range from insufficient data access control to network security vulnerabilities,and from lack of physical security to data information leakage.At the computation level,issues involve non-representative bias in data selection,subjectivity bias in data annotation,algorithmic amplification bias within models,and feedback loop bias within models.Therefore,it is imperative to optimize data collection processes,strengthen the supervision of data usage,reinforce data security regulations,enhance the transparency and interpretability of algorithms,and improve adaptive dynamic content governance systems to establish a secure,fair and free digital space.
generative artificial intelligencedata riskrisk governancedata securitydata supervisionalgorithmic biasdigital space
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