基于行为经济学理论的数据治理新方案

Bo Hou
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引用次数: 0

摘要

数字经济已成为全球GDP中最重要的部门之一。个人数据是通过网络技术和人工智能的应用创造价值的新资产类别。然而,人们越来越关注与个人数据利用相关的隐私侵犯和人权侵犯。各国制定了各种数据法,以平衡数据流动性和隐私保护。然而,大多数法律都存在固有的局限性和执行不力的问题,无法实现其目标和保护原则。本文运用行为经济学的理论框架,对信息不对称、有限理性、权力失衡和技术能力丧失的问题及其成因进行了分类。该研究通过提出一种全球数据治理方案来解决数据法的局限性,做出了新颖的贡献。该方案采用了自由意志主义的家长制方法,并在框架设计中发展了七个原则。方案中的元素和组件包括个人、数据控制器、隐私评级框架、元数据和隐私配置、报告、自动同意管理(ACM)、局和签名等。这些组件将在一个可互操作的全球数据管理平台上运行。开发可视化图表来描述组件和过程之间各种形式的交互。通过普通数据请求、敏感数据请求、不一致检查、数据权利行使、监控数据传输、广播和通知等实验场景,找到隐私保护与数据流动性之间的平衡。所分析的场景并不详尽,但作为一个有意义的起点,可以激发学者们更多的设计和讨论。
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A Novel Data Governance Scheme Based on the Behavioral Economics Theory
The digital economy has become one of the most important sectors in global GDP.Personal data is the new asset class that creates value through the applications ofcybertechnologies and Artificial Intelligence. However, there are increasing concerns over the privacy invasions and human rights violations associated with the exploitation ofpersonal data.Various data laws were made in nations to balance the data fluidity and privacy protections. However, most laws have inherent limitations and underenforcement issuesthat fail to achieve their aims and protection principles. Utilizing a behavioral economics theoretical framework, this study categorizes the issues and causes to InformationAsymmetry, Bounded Rationality, Power Imbalance, and Technical Incapacity.The study makes a novel contribution by proposing a global data governance scheme to address the limitations of data laws. The scheme adopts a Libertarian Paternalism approach and develops seven principles in the framework design. Elements and components in the scheme include individuals, data controllers, privacy rating frameworks, meta-data and privacy configuration, reports, Automated Consent Management (ACM), Bureaus, and signatures, etc. The components will operate on an interoperable and global data management platform. Visual diagrams are developed to describe the various forms of interactions between components and procedures.A balance between privacy protection and data fluidity is found through experimental scenarios such as Ordinary Data Request, Sensitive Data Request, Inconsistency Checks, Data Rights Exercise, Monitored Data Transfer, Broadcast and Notice. The scenarios analyzed are not exhaustive but serve as the meaningful startingpoint to inspire more designs and discussions from scholars.
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