Data Consultations, Racism, and Critiquing Colonialism in Demographic Datasheets

Abstract

Objective: We consider how data librarians can take antiracist action in education and consultations. We attempt to apply QuantCrit thinking, particularly to demographic datasheets. Methods: We synthesize historical context with modern critical thinking about race and data to examine the origins of current assumptions about data. We then present examples of how racial categories can hide, rather than reveal, racial disparities. Finally, we apply the Model of Domain Learning to explain why data science and data management experts can and should expose experts in subject research to the idea of critically examining demographic data collection. Results: There are good reasons why patrons who are experts in topics other than racism can find it challenging to change habits from Interoperable approaches to race. Nevertheless, the Census categories explicitly say that they have no basis in research or science. Therefore, social justice requires that data librarians should expose researchers to this fact. If possible, data librarians should also consult on alternatives to habitual use of the Census racial categories. Conclusions: We suggest that many studies are harmed by including race and should remove it entirely. Those studies that are truly examining race should reflect on their research question and seek more relevant racial questions for data collection.

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