With this knowledge, we call for users of administrative data and those building data integrationcapacity to center racial equity in their data practices. We call for the inclusion of community voicesand power sharing at every stage of the data life cycle. We call for relationship building amongthose represented in the data and those stewarding and using the data. Without a deliberate effortto address structural racism, institutional racism, and unrecognized bias, data integration willinevitably reproduce and exacerbate existing harm.To avoid this, we must embed considerations of racial equity throughout the data life cycle:In planningIn data collectionIn data accessIn data analysisIn the use of algorithms & arti昀椀cial intelligenceIn reporting & disseminationAcknowledging history, harm, and the potentially negative implications of data integration for groupsmarginalized by inequitable systems is a key 昀椀rst step, but it is only a 昀椀rst step. To go beyond this, wemust 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.INTRODUCTIONWe are at a pivotal moment, one in which the use of data is accelerating in both exciting andconcerning ways. While we have access to greater amounts of data than at any other point inour 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.”5We are working to create a new kind of datainfrastructure—one that dismantles “feedback loopsof injustice”6 and instead shares power and knowledgewith 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.3
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