Measurement is an exercise of power. When a municipality attempts to address inequality, it relies on geographic and demographic data to define the scope of the problem. But data is never "raw." The categories used by the Census, the boundaries drawn around neighborhoods, and the algorithms used to target public services inherently center the perspective of the institution doing the measuring. This module moves past "data equity" (trying to achieve fairness inside an extractive system) and introduces "data justice": the radical assertion that marginalized groups must hold sovereign power over the data generated by their communities. A key feature of this is understanding the scale effect: because spatial boundaries are arbitrary, public data can be gerrymandered to erase local realities.
In This Module
- Covers: The difference between Data Equity and Data Justice, Indigenous Data Sovereignty, the CARE Principles, and the political consequences of the Modifiable Areal Unit Problem (MAUP).
- Why it matters: State institutions regularly use "impartial" demographic data to justify the structural exclusion of marginalized groups. If organizers cannot critique the methodology of the data collection, they cannot challenge the systemic outcome.
- After this module, the reader can: Audit public data sets for extractive practices and articulate the demands for community data sovereignty using the CARE protocol.
Reading List
Start Here
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A highly accessible but theoretically rigorous examination of how data systems encode and amplify structural inequality. crucially for this course, it highlights how map-making and boundary-drawing (the scale problem) are intensely political acts. The choice of where to draw a line determines what demographic reality becomes visible to the state.
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This foundational paper establishes the CARE principles (Collective benefit, Authority to control, Responsibility, Ethics) as a framework for Indigenous Data Sovereignty. It represents a paradigm shift from the open-data movement (FAIR), demanding that communities—not just state researchers—hold the ultimate authority over how information about their people and lands is extracted and deployed.
Going Deeper
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Benjamin explores how systemic racism is seamlessly ported into the algorithmic tools that increasingly run municipal governance, public health, and criminal justice. This book teaches practitioners how to spot "the New Jim Code"—the use of seemingly neutral "objective" data to hide ongoing structural bias and shut down public, democratic oversight.
For Practitioners
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A vital operational guide for building civic technology and community data systems. Costanza-Chock provides a framework for shifting from designing "for" marginalized communities to designing "with" and "by" them, ensuring the community retains structural control over the final product rather than acting as mere focus groups.
Key Concepts
How do data systems encode structural inequality according to data feminism?
Catherine D'Ignazio and Lauren Klein demonstrate in Data Feminism that data is never "raw"—every dataset reflects the perspective and power of the institution that collected it. Map-making and boundary-drawing are intensely political acts: the choice of where to draw a line around a census tract, neighborhood, or school district determines what demographic reality becomes visible to the state and what is erased. Data systems routinely encode and amplify structural inequality by normalizing the categories and spatial units that benefit dominant groups.
What are the CARE Principles for Indigenous Data Governance and how do they differ from FAIR data principles?
The CARE Principles—Collective benefit, Authority to control, Responsibility, and Ethics—established by Stephanie Russo Carroll and colleagues, represent a paradigm shift from the open-data movement's FAIR principles (Findable, Accessible, Interoperable, Reusable). While FAIR focuses on making data maximally available for researchers, CARE demands that communities—not state researchers—hold ultimate authority over how information about their people and lands is extracted, stored, and deployed.
What is the 'New Jim Code' and how does algorithmic bias undermine democratic oversight?
Ruha Benjamin's concept of the "New Jim Code" describes how systemic racism is seamlessly ported into the algorithmic tools that increasingly govern municipal services, public health, and criminal justice. Predictive policing algorithms, automated benefit eligibility systems, and risk-scoring tools use seemingly neutral "objective" data to reproduce racial disparities while providing institutions with a veneer of scientific impartiality that shuts down public democratic oversight and accountability.
What does design justice mean and how does it differ from designing 'for' marginalized communities?
Sasha Costanza-Chock's design justice framework shifts from designing "for" marginalized communities to designing "with" and "by" them. Traditional civic technology development treats affected communities as focus groups—soliciting feedback but retaining all design authority. Design justice demands that the community retains structural control over the final product, ensuring technology and data systems serve community-defined priorities rather than institutional convenience.
Goal: Incorporate a Data Justice evaluation into your Community Democratic Health Profile.
Identify a major dataset that your local government relies on to manage the community you defined in Module 1. (This could be predictive policing maps, school districting algorithms, unhoused population surveys, or municipal health outcomes).
- The Scale Check: Look closely at the geographic units (precincts, census tracts, zip codes) being utilized by the data set. How do these chosen boundaries blur, divide, or hide the true lived boundaries of your community?
- The CARE Audit: Test the municipal practice against the CARE principles. Does the dataset provide Collective benefit to your community, or is it purely extractive? Does the community possess the Authority to control how this data is shared with private entities or federal agencies?
Add this Data Audit to your Profile. The ability to push back against extractive state measurement is a fundamental requirement of inclusive democracy.