Misclassification and Bias in Predictions of Individual Ethnicity from Administrative Records

We show that a common method of predicting individuals’ race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups...

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Bibliographic Details
Published in:The American political science review 2024-05, Vol.118 (2), p.1058-1066
Main Authors: ARGYLE, LISA P., BARBER, MICHAEL
Format: Article
Language:English
Subjects:
Administrative records
Algorithms
Archives & records
Asian people
Asians
Bayesian analysis
Bias
Censuses
Classification
Correlation
Demography
Errors
Ethnic groups
Ethnicity
Geographic Location
Hispanic Americans
Imbalance
Letter
Minority groups
Neighborhoods
Race
Racial Composition
Self report
Sociodemographics
Socioeconomic factors
Socioeconomic Status
Voter registration
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Summary:We show that a common method of predicting individuals’ race in administrative records, Bayesian Improved Surname Geocoding (BISG), produces misclassification errors that are strongly correlated with demographic and socioeconomic factors. In addition to the high error rates for some racial subgroups, the misclassification rates are correlated with the political and economic characteristics of a voter’s neighborhood. Racial and ethnic minorities who live in wealthy, highly educated, and politically active areas are most likely to be misclassified as white by BISG. Inferences about the relationship between sociodemographic factors and political outcomes, like voting, are likely to be biased in models using BISG to infer race. We develop an improved method in which the BISG estimates are incorporated into a machine learning model that accounts for class imbalance and incorporates individual and neighborhood characteristics. Our model decreases the misclassification rates among non-white individuals, in some cases by as much as 50%.
ISSN:0003-0554
1537-5943
DOI:10.1017/S0003055423000229