{"title":"A Novel Data Governance Scheme Based on the Behavioral Economics Theory","authors":"Bo Hou","doi":"10.2139/ssrn.3773565","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":319022,"journal":{"name":"Economics of Networks eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Economics of Networks eJournal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3773565","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.