Data Refusal From Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design
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3630107.pdf
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287.53 KB
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Adobe PDF
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eb2cdd964c692d05a1d782c9ddc5ce57
Author(s)
Zong, Jonathan
Matias, J Nathan
Journal
ACM Journal on Responsible Computing
Publisher
ACM
Citation
Zong, Jonathan and Matias, J Nathan. "Data Refusal From Below: A Framework for Understanding, Evaluating, and Envisioning Refusal as Design." ACM Journal on Responsible Computing.
Version
Final published version
Abstract
Amidst calls for public accountability over large data-driven systems, feminist and indigenous scholars have developed refusal as a practice that challenges the authority of data collectors. However, because data affects so many aspects of daily life, it can be hard to see seemingly different refusal strategies as part of the same repertoire. Furthermore, conversations about refusal often happen from the standpoint of designers and policymakers rather than the people and communities most affected by data collection. In this paper, we introduce a framework for data refusal from below—writing from the standpoint of people who refuse, rather than the institutions that seek their compliance. Because refusers work to reshape socio-technical systems, we argue that refusal is an act of design, and that design-based frameworks and methods can contribute to refusal. We characterize refusal strategies across four constituent facets common to all refusal, whatever strategies are used: autonomy, or how refusal accounts for individual and collective interests; time, or whether refusal reacts to past harm or proactively prevents future harm; power, or the extent to which refusal makes change possible; and cost, or whether or not refusal can reduce or redistribute penalties experienced by refusers. We illustrate each facet by drawing on cases of people and collectives that have refused data systems. Together, the four facets of our framework are designed to help scholars and activists describe, evaluate, and imagine new forms of refusal.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3630107
