The data subject and the myth of the ‘black box’ data communication and critical data literacy as a resistant practice to platform exploitation

Dennis Nguyen*, Bjorn Beijnon

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

This conceptual paper explores the role of communication around data practices of Big Tech companies. By critiquing communication practices, we argue that Big Tech platforms shape users into data subjects through framing, influencing behaviour, and the black-boxing of algorithms. We approach communication about data from three perspectives: (1) current data communication constructs reductive data identities for users and contributes to the colonization of daily routines; (2) by strategically deploying the black box metaphor, tech companies try to legitimize abuses of power in datafication processes; (3) the logic in which communication is mediated through the interfaces of Big Tech platforms is normalizing this subjectification. We argue that critical data literacy can foster individual resilience and allows users to resist exploitative practices, but this depends on transparent communication. The opposite seems standard among tech companies that obfuscate their data practices. Current commercial appropriations of data ethics need to be critically assessed against the background of increasing competition in the digital economy
Original languageEnglish
Pages (from-to)333-349
Number of pages17
JournalInformation Communication and Society
Volume27
Issue number2
Early online date26 Apr 2023
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Funding

This work was supported by Nederlandse Organisatie voor Wetenschappelijk Onderzoek.

FundersFunder number
Nederlandse Organisatie voor Wetenschappelijk Onderzoek

    Keywords

    • Big Tech
    • Data communication
    • critical data literacy
    • data identities
    • data practices

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