Data leverage: A framework for empowering the public in its relationship with technology companies

Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor, Brent J Hecht

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Many powerful computing technologies rely on implicit and explicit data contributions from the public. This dependency suggests a potential source of leverage for the public in its relationship with technology companies: by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of many lucrative technologies. In this paper, we synthesize emerging research that seeks to better understand and help people action this data leverage. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and accountability in computing, we present a framework for understanding data leverage that highlights new opportunities to change technology company behavior related to privacy, economic inequality, content moderation and other areas of societal concern. Our framework also points towards ways that policymakers can bolster data leverage as a means of changing the balance of power between the public and tech companies.

Original languageEnglish (US)
Title of host publicationFAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
PublisherAssociation for Computing Machinery, Inc
Pages215-227
Number of pages13
ISBN (Electronic)9781450383097
DOIs
StatePublished - Mar 3 2021
Event4th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2021 - Virtual, Online, Canada
Duration: Mar 3 2021Mar 10 2021

Publication series

NameFAccT 2021 - Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency

Conference

Conference4th ACM Conference on Fairness, Accountability, and Transparency, FAccT 2021
Country/TerritoryCanada
CityVirtual, Online
Period3/3/213/10/21

Bibliographical note

Funding Information:
This work was funded in part by NSF grants 1815507 and 1707296. We are grateful for feedback from colleagues at the CollabLab at Northwestern, GroupLens at the University of Minnesota, and the Community Data Science Collective.

Publisher Copyright:
© 2021 ACM.

Keywords

  • Conscious data contribution
  • Data leverage
  • Data poisoning
  • Data strikes

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