Information asymmetries: recognizing the limits of the GDPR on the data-driven market

被引:20
|
作者
van de Waerdt, Peter J. [1 ]
机构
[1] Univ Groningen, Secur Technol & E Privacy STeP Res Grp, Groningen, Netherlands
关键词
Information Asymmetry; Data-driven companies; Behavioral profiling; Transparency; Data protection; General Data Protection Regulation;
D O I
10.1016/j.clsr.2020.105436
中图分类号
D9 [法律]; DF [法律];
学科分类号
0301 ;
摘要
Online search engines, social media platforms, and targeted advertising services often employ a "data-driven" business model based on the large-scale collection, analysis, and monetization of personal data. When providing such services significant information asymmetries arise: data-driven companies collect much more personal data than the consumer knows or can reasonably oversee, and data-driven companies have much more (technical) information about how this data is processed than consumers would be able to understand. This article demonstrates the vulnerable position consumers continue to find themselves in as a result of information asymmetries between them and data-driven companies. The GDPR, by itself, is in practice unable to mitigate these information asymmetries, nor would it be able to provide for effective transparency, since it does not account for the unique characteristics of the data-driven business model. Consumers are thus faced with an insurmountable lack of transparency which is inherent in, as well as the inevitable consequence of, the magnitude of the information asymmetries present on the data-driven market. (C) 2020 Peter J. van de Waerdt. Published by Elsevier Ltd.
引用
收藏
页数:18
相关论文
共 50 条
  • [1] Data-driven information for action
    Wulff, Kristin
    Finnestrand, Hanne
    [J]. GIO-GRUPPE-INTERAKTION-ORGANISATION-ZEITSCHRIFT FUER ANGEWANDTE ORGANISATIONSPSYCHOLOGIE, 2023, 54 (01): : 65 - 77
  • [2] A Data-Driven Analysis of Blockchain Systems' Public Online Communications on GDPR
    Saglam, Rahime Belen
    Aslan, Cagri Burak
    Li, Shujun
    Dickson, Lisa
    Pogrebna, Ganna
    [J]. 2020 IEEE INTERNATIONAL CONFERENCE ON DECENTRALIZED APPLICATIONS AND INFRASTRUCTURES (DAPPS 2020), 2020, : 22 - 31
  • [3] On Enabling GDPR Compliance in Business Processes Through Data-Driven Solutions
    Zaman R.
    Hassani M.
    [J]. SN Computer Science, 2020, 1 (4)
  • [4] Data Subject Rights under the GDPR: With a Commentary through the Lens of the Data-driven Economy
    Drechsler, Laura
    [J]. EUROPEAN LAW REVIEW, 2022, 47 (01) : 149 - 150
  • [5] On the Limits of Data-Driven Cancer Prognosis Prediction
    Yousefi, Mohammadmahdi R.
    Dalton, Lori A.
    [J]. 2016 3RD IEEE EMBS INTERNATIONAL CONFERENCE ON BIOMEDICAL AND HEALTH INFORMATICS, 2016, : 537 - 540
  • [6] Data-driven approaches to information access
    Dumais, S
    [J]. COGNITIVE SCIENCE, 2003, 27 (03) : 491 - 524
  • [7] Information acquisition from data-driven analytics: A perspective of blockchain service in a duopoly market
    Xu, Lang
    Luo, Yuqi
    Pu, Xujin
    [J]. COMPUTERS & INDUSTRIAL ENGINEERING, 2023, 176
  • [8] Technologies of Speculation: The Limits of Knowledge in a Data-Driven Society
    Von Feldt, Paige Anne
    [J]. INTERNATIONAL JOURNAL OF COMMUNICATION, 2022, 16 : 2634 - 2636
  • [9] Technologies of Speculation: The Limits of Knowledge in a Data-Driven Society
    Leshnick, Anat
    [J]. SURVEILLANCE & SOCIETY, 2023, 21 (03) : 343 - 344
  • [10] The Limits of Empiricism: A Critique of Data-Driven Theory Development
    Van Slyke, Craig
    Kamis, Arnold
    [J]. DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS, 2024, 55 (02): : 119 - 145