2020
DOI: 10.48550/arxiv.2012.09995
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Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies

Abstract: Many powerful computing technologies rely on data contributions from the public. This dependency suggests a potential source of leverage: by reducing, stopping, redirecting, or otherwise manipulating data contributions, people can influence and impact the effectiveness of these technologies. In this paper, we synthesize emerging research that helps people better understand and action this data leverage. Drawing on prior work in areas including machine learning, human-computer interaction, and fairness and acco… Show more

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Cited by 1 publication
(1 citation statement)
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References 47 publications
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“…These include First Nations Peoples seeking data sovereignty (Walter et al, 2021) and not providing their Indigenous status in data collectionsessentially resisting data supply-where they feel the data use is potentially discriminating and not in their interests (Australian Bureau of Statistics 2012, p. 11). Other examples include citizen census boycotts and cancellations (Kitchin and Lauriault 2014, p. 9); students resisting algorithmic grading (Jones and Safak, 2020); welfare recipients and advocates resisting automated debt collection based on data matching (Whiteford, 2020); and in the commercial sphere consumers reducing their digital footprint or purposefully inputting misleading data (Vincent et al, 2021), or taking their business and data to providers with more acceptable terms (Doffman, 2021;Vincent et al, 2021).…”
Section: Resistancementioning
confidence: 99%
“…These include First Nations Peoples seeking data sovereignty (Walter et al, 2021) and not providing their Indigenous status in data collectionsessentially resisting data supply-where they feel the data use is potentially discriminating and not in their interests (Australian Bureau of Statistics 2012, p. 11). Other examples include citizen census boycotts and cancellations (Kitchin and Lauriault 2014, p. 9); students resisting algorithmic grading (Jones and Safak, 2020); welfare recipients and advocates resisting automated debt collection based on data matching (Whiteford, 2020); and in the commercial sphere consumers reducing their digital footprint or purposefully inputting misleading data (Vincent et al, 2021), or taking their business and data to providers with more acceptable terms (Doffman, 2021;Vincent et al, 2021).…”
Section: Resistancementioning
confidence: 99%