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1.中国政法大学法与经济学研究院,北京 100088
2.中国信息通信研究院,北京 100191
Received:18 April 2024,
Revised:2024-07-29,
Published:15 September 2024
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迟舜雨,杜安琪.生成式人工智能敏捷治理的国际趋势与建构模式[J].智能科学与技术学报,2024,06(03):402-412.
CHI Shunyu,DU Anqi.International trends and constructive models of agile governance for generative artificial intelligence[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):402-412.
迟舜雨,杜安琪.生成式人工智能敏捷治理的国际趋势与建构模式[J].智能科学与技术学报,2024,06(03):402-412. DOI: 10.11959/j.issn.2096-6652.202434.
CHI Shunyu,DU Anqi.International trends and constructive models of agile governance for generative artificial intelligence[J].Chinese Journal of Intelligent Science and Technology,2024,06(03):402-412. DOI: 10.11959/j.issn.2096-6652.202434.
敏捷治理作为一种新的治理范式,有助于提升生成式人工智能治理水平。通过分析生成式人工智能的治理需求与发展挑战,揭示了传统治理模式的不足,并认为以多元共治、灵活的治理依据和迭代过程为核心特征的敏捷治理能够有效平衡技术创新与风险控制的需求,可以弥补传统治理范式在生成式人工智能治理的局限。在审视了国际敏捷治理的实践后,提出了适用于中国的生成式人工智能敏捷治理策略建议。为保障中国生成式人工智能的高质量发展和高水平安全,应在多元参与的基础上进一步发展多元共治模式,加强硬法与软法之间的互动,并逐步丰富生成式人工智能的治理工具箱。
Agile governance
as a new paradigm of governance
is conducive to enhancing the governance level of generative artificial intelligence. By analyzing the governance needs and development challenges of generative artificial intelligence
the inadequacies of traditional governance models were revealed. It was believed that agile governance
characterized by multi-stakeholder co-governance
flexible governance basis
and iterative processes
could effectively balance the needs of technological innovation and risk control
and compensate for the limitations of traditional governance paradigms in the governance of generative artificial intelligence. After examining the international practices of agile governance
suggestions for agile governance strategies suitable for generative artificial intelligence in China were proposed. To ensure the high-quality development and high-level safety of generative artificial intelligence in China
it is necessary to further develop a multi-stakeholder co-governance model on the basis of multi-participation
strengthen the interaction between hard law and soft law
and gradually enrich the governance toolkit of generative artificial intelligence.
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