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作 者:何异之 宋凡 He Yizhi;Song Fan(School of Law,Hunan University of Technology and Business,Changsha Hunan 410000,China)
出 处:《衡阳师范学院学报》2025年第2期53-59,共7页Journal of Hengyang Normal University
基 金:国家社会科学基金一般项目“共同富裕宪法基础中的特殊群体社会权保障研究”(22BFX114);湖南省教育厅科学研究优秀青年项目“人工智能发展的法治化保障研究”(24B0589)。
摘 要:作为人格权在数字领域的延伸,信息自决权是个人对自身数据控制的权利化表达。信息自决权的权利主体为自然人,在《中华人民共和国宪法》《中华人民共和国民法典》《中华人民共和国个人信息保护法》中均有规定。信息自决权的保护主要面临着算法技术诱发匿名化个人信息再识别、算法自动化决策冲击知情同意规则以及算法个性化推荐与意思自治相矛盾等问题。对此,应确立信息自决权权利束的保护价值,实现对个人信息利益整体性保护;通过最小必要原则、比例原则等法律原则限定权利行使边界;从法律与技术层面完善匿名化制度,减少匿名信息被重新识别的可能性;创新告知方式和明确同意内容,确保信息主体始终掌握数据处理的完整进程;构建专门的算法监管机构,保障算法自动化决策的客观性与公平性。As an extension of personality rights in the digital realm,the right to informational self-determination constitutes the juris⁃prudential manifestation of an individual’s control over their personal data.The subject of this right is exclusively limited to natural persons,which stipulated in the Constitution,the Civil Code,and the Personal Information Protection Law.The protection of the right to self-determination of information mainly faces issues such as algorithmic technology induced anonymization of personal information for re-identification,algorithmic automation of decision-making impacting informed consent rules,and the contradiction between algorith⁃mic personalized recommendation and autonomy of will.To effectively address these issues,it is imperative to establish the protection value of the right to self-determination of information and achieve the overall protection of personal information interests.Defining the boundaries of the exercise of rights through legal principles such as the principle of least necessity and proportionality,improving the anonymity system from a legal and technical perspective to reduce the possibility of anonymous information being re-identified,inno⁃vating notification methods and clarify consent content to ensure that information subjects always have a complete understanding of the data processing process,and establishing a specialized algorithm regulatory agency to ensure the objectivity and fairness of algorithm automation decision-making.
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