Algorithm Power and Legal Boundaries: Rights Conflicts and Governance Responses in the Era of Artificial Intelligence

Jinghui He () and Zhenyang Zhang ()
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Jinghui He: Department of Law, Dongguk University, Seoul 04620, Republic of Korea
Zhenyang Zhang: Department of Law, Dongguk University, Seoul 04620, Republic of Korea

Laws, 2025, vol. 14, issue 4, 1-28

Abstract: This study explores the challenges and theoretical transformations that the widespread application of AI technology in social governance brings to the protection of citizens’ fundamental rights. By examining typical cases in judicial assistance, technology-enabled law enforcement, and welfare supervision, it explains how AI characteristics such as algorithmic opacity, data bias, and automated decision-making affect fundamental rights including due process, equal protection, and privacy. The article traces the historical evolution of privacy theory from physical space protection to informational self-determination and further to modern data rights, pointing out the inadequacy of traditional rights-protection paradigms in addressing the characteristics of AI technology. Through analyzing AI-governance models in the European Union, the United States, Northeast Asia, and international organizations, it demonstrates diverse governance approaches ranging from systematic risk regulation to decentralized industry regulation. With a special focus on China, the article analyzes the special challenges faced in AI governance and proposes specific recommendations for improving AI-governance paths. The article argues that only within the track of the rule of law, through continuous theoretical innovation, institutional construction, and international cooperation, can AI technology development be ensured to serve human dignity, freedom, and fair justice.

Keywords: artificial intelligence; social governance; fundamental rights; algorithmic decision-making; legal boundaries (search for similar items in EconPapers)
JEL-codes: D78 E61 E62 F13 F42 F68 K0 K1 K2 K3 K4 (search for similar items in EconPapers)
Date: 2025
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