AISP Toolkit Feb25 2025 - Flipbook - Page 7
With this knowledge, we call for users of administrative data and those building data integration
capacity to center racial equity in their data practices. We call for the inclusion of community voices
and power sharing at every stage of the data life cycle. We call for relationship building among
those represented in the data and those stewarding and using the data. Without a deliberate effort
to address structural racism, institutional racism, and unrecognized bias, data integration will
inevitably reproduce and exacerbate existing harm.
To avoid this, we must embed considerations of racial equity throughout the data life cycle:
In planning
In data collection
In data access
In data analysis
In the use of algorithms & arti昀椀cial intelligence
In reporting & dissemination
Acknowledging history, harm, and the potentially negative implications of data integration for groups
marginalized by inequitable systems is a key 昀椀rst step, but it is only a 昀椀rst step. To go beyond this, we
must center the voices, stories, expertise, and knowledge of these communities in decision-making,
and take collective action with shared power to improve outcomes and harness data for social good.
INTRODUCTION
We are at a pivotal moment, one in which the use of data is accelerating in both exciting and
concerning ways. While we have access to greater amounts of data than at any other point in
our history, privacy laws and data governance practices lag behind, placing Black, Indigenous,
and communities of color at the greatest risk of the “data-i昀椀cation of injustice.”5
We are working to create a new kind of data
infrastructure—one that dismantles “feedback loops
of injustice”6 and instead shares power and knowledge
with those who need systems change the most.
Will you join us?
5 Benjamin, R. (2019)
6 Eubanks, V. (2018). Automating Inequality: How high-tech tools pro昀椀le, police, and punish the poor. St. Martin's Press.
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