Abstract
In the context of the rapid development of generative artificial intelligence (AIGC), the fair use system faces many challenges, especially in the process of data mining and machine learning, and its applicability is significantly limited. The fair use system is based on the "three-step" judgment standard proposed by the Berne Convention, which requires that the use of a work must be carried out under specific and non-universal circumstances, and must not harm the normal use of the work or infringe the legitimate rights and interests of the author without cause. However, generative artificial intelligence often fails to meet these conditions through the collection and processing of massive data. For example, data cleaning and sorting in the machine learning stage may affect the normal use of the work, while the behavior of data mining may also infringe the market value of the work. In addition, even under the framework of the "four-step judgment method" in the United States, fair use is limited to non-commercial research and development, which is obviously not conducive to the innovation and development of the increasingly commercialized generative artificial intelligence.
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