Bottom-Up Organizing with Tools from On High: Understanding the Data Practices of Labor Organizers

Crowdsourcing Task Design & Quality ControlAlgorithmic Fairness & BiasEmpowerment of Marginalized GroupsSocial WorkersRefugee & Immigrant Service ProvidersGovernment Officials & Civil Servants

This paper provides insight into the use of data tools in the American labor movement by analyzing the practices of staff employed by unions to organize alongside union members. We interviewed 23 field-level staff organizers about how they use data tools to evaluate membership. We find that organizers work around and outside of these tools to develop access to data for union members and calibrate data representations to meet local needs. Organizers mediate between local and central versions of the data, and draw on their contextual knowledge to challenge campaign strategy. We argue that networked data tools can compound field organizers' lack of discretion, making it more difficult for unions to assess and act on the will of union membership. We show how the use of networked data tools can lead to less accurate data, and discuss how bottom-up approaches to data gathering can support more accurate membership assessments.

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https://hci.top/en/papers/chi/32171/2020

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DOI: https://doi.org/10.1145/3313831.3376185
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Source
CHI
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Year
2020
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Authors
3 authors
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Subtopics
Crowdsourcing Task Design & Quality Control, Algorithmic Fairness & Bias, Empowerment of Marginalized Groups
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Professions
Social Workers, Refugee & Immigrant Service Providers, Government Officials & Civil Servants
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Abstract only
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Related Papers
1 related papers