Disaggregating data by race and ethnicity is a critical method for shining light on racialized systems of privilege and oppression. Imputation is a powerful tool for disaggregating data by generating racial and ethnic identifiers onto datasets lacking this information. But if used without a proactive focus on equity, it can harm Black people, Indigenous people, and other people of color. In this talk, we will share lessons we learned from a case study in which we proactively incorporated equity in imputing race and ethnicity onto a nationally representative sample of credit bureau data. We organize these lessons around a set of “ethics checkpoints” that researchers, analysts, and practitioners can use to identify and address potential racial bias and inaccuracy: checkpoint 1: before imputation, audit input data for bias; checkpoint 2: during imputation, examine where bias could be introduced at each step; and checkpoint 3: after imputation, assess whether imputed race/ethnicity data are accurate enough to used ethically for your analytic purpose.
Speakers: Alena Stern and Ajjit Narayanan, Urban Institute
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