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生成式人工智能商业秘密保护困境及对策

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生成式人工智能以大语言模型为基础,与传统的人工智能相比可以执行更加多样化的任务,其作为商业秘密进行保护具有保护范围广、保护成本低、保护期限更长等优势。但也存在缺乏商业秘密客体保护范围规定,合理保密措施认定标准不明确、例外规定不完善等困境。为了更好地发挥商业秘密保护的作用,需要进一步完善相关法律法规,明确商业秘密保护客体范围、合理保密措施认定标准和例外规定,更好地保护生成式人工智能权利人的商业秘密。
Dilemma and Countermeasure of Trade Secret Protection by Generative Artificial Intelligence
Based on large language model,generative artificial intelligence can perform more diverse tasks than traditional artificial intelligence.As a trade secret,generative artificial intelligence has the advantages of wide protection range,low protection cost and longer protection period.However,there are also some difficulties,such as lack of scope of trade secret object protection,unclear identification standards of reasonable confidentiality measures and imperfect exception provisions.In order to better play the role of trade secret protection,it is necessary to further improve relevant laws and regulations,clarify the scope of trade secret protection objects,the identification standards and exceptions of reasonable confidentiality measures,and better protect the trade secrets of generative artificial intelligence right holders.

generative artificial intelligencetrade secretsalgorithm interpretationintellectual property

朱文玉、王舒欣

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东北林业大学 文法学院,黑龙江 哈尔滨 150040

生成式人工智能 商业秘密 算法解释 知识产权

2024

黑河学院学报
黑河学院

黑河学院学报

影响因子:0.169
ISSN:1674-9499
年,卷(期):2024.15(7)