Citation:
JI Weidong and ZHAO Zerui. How Can Law Realize Dynamic Governance——Legislative Responses to the Uncertainties of Artificial Intelligence[J]. Academic Monthly, 2025, 57(3): 98-109.
How Can Law Realize Dynamic Governance——Legislative Responses to the Uncertainties of Artificial Intelligence
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Abstract
The uncertainty of artificial intelligence not only serves as the consensus-driven motivation behind the global wave of artificial intelligence legislation but also acts as the divergent factor that triggers the multifarious legislative practices across nations.The European Union,the United States,and Japan have respectively formed three distinct legislative models:risk regulation emphasizing government supervision,industry self-governance emphasizing market dominance,and soft law guidance focusing on government-enterprise cooperation.However,all these three legislative models view law as a tool for the government to enforce specific governance concepts,thereby generating an overconfidence that either the government or the market can independently handle the uncertainties of artificial intelligence.For China’s unified legislation on artificial intelligence to overcome the predicament where a single entity finds it difficult to regulate uncertainties and to achieve the dynamic governance goal based on communication among multiple subjects,it is necessary to rely on democratic communication based on risk symmetry rather than planned regulation to control uncertainties.In this regard,the unified legislation on artificial intelligence can be based on the institutional design of communication procedures to urge the government to eliminate risk asymmetry in the market,ensuring that multiple stakeholders in specific scenarios make agile and accountable risk decisions through risk communication under conditions of risk symmetry,thereby achieving dynamic governance of risk sharing.
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[1]
WAN Yong
, LI Yalan
. Realizing the Function of Copyright Law in the Era of Artificial Intelligence. Academic Monthly,
2026, 58(7): 104-116.
-
[2]
YOU Yang
. How Does AI Understand Language Meaning. Academic Monthly,
2026, 58(8): 16-28.
-
[3]
WANG Qian
. AI-Generated Content and Copyright Liability: Paradoxes and Resolutions. Academic Monthly,
2026, 58(8): 118-132.
-
[4]
HE Da'an
, LI Huaizheng
. Digital Adjustment Mechanism in the Application of Artificial General Intelligence. Academic Monthly,
2026, 58(7): 54-63.
-
[5]
LIN Xifen
, FU Zhangyi
. How Does Public Trust in the Judiciary Shape Acceptability of Judges' AI-assisted Adjudications——An Empirical Study Based on Questionnaire Data. Academic Monthly,
2026, 58(8): 133-144.
-
[6]
ZHANG Yi
. Cognitive Transformation in Historical Research in the Age of AI——“Fluency-Induced Occlusion” in LLM-Assisted Reading of Diplomatic Manuscripts. Academic Monthly,
2026, 58(8): 29-40.
-
[7]
XIN Fangkun
. Agile Governance for Grassroots Cross-boundary Challenges. Academic Monthly,
2026, 58(3): 91-101.
-
[8]
SUO Liming
, WEI He'ning
. Platform Transition: The Re-Evolution of Network Governance Model. Academic Monthly,
2026, 58(7): 90-103.
-
[9]
XIE Yue
. How State Capacity Is Generated in Practice——A Dynamic Equilibrium Model of Governance. Academic Monthly,
2026, 58(8): 106-117.
-
[10]
DING Huiping
. Invisible Governance and the Rebuilding of Local Social Order: A Historical Sociology Analysis of Qi Biaojia's Famine Relief Action in Late Ming Dynasty. Academic Monthly,
2026, 58(3): 143-154.
-
[11]
XIONG Changkun
, ZHANG Ziyuan
. Multi-party Game and Informal Governance in Central-Local Fiscal Relations——Focusing on the Yinhao Reform of Guangdong Salt Tax in the Early Republic of China. Academic Monthly,
2026, 58(5): 198-210.
-
[12]
XU Xiangdong
. Consciousness and The Moral Status of AI Agents. Academic Monthly,
2026, 58(8): 5-15.
-
[13]
LIU Chuang
, LIU Sijia
. Conceptual Engineering: A Way of Understanding Mind and Intelligence in the Future. Academic Monthly,
2026, 58(1): 11-19.
-
-
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