2022/06/26 by Ari Decter-Frain, Decter-Frain, Ari · 1 citation
Social Sciences · #Electoral Systems and Political Participation #FOS: Computer and information sciences #Machine Learning (cs.LG) #Names, Identity, and Discrimination Research #Racial and Ethnic Identity Research
paper · pdf · doi:10.48550/arxiv.2206.14583
openalex publication_date 2022/06/26 · openalex created_date 2022/08/03 · openalex updated_date 2026/07/28
Bayesian Improved Surname Geocoding (BISG) is the most popular method for proxying race/ethnicity in voter registration files that do not contain it. This paper benchmarks BISG against a range of previously untested machine learning alternatives, using voter files with self-reported race/ethnicity from California, Florida, North Carolina, and Georgia. This analysis yields three key findings. First, machine learning consistently outperforms BISG at individual classification of race/ethnicity. Second, BISG and machine learning methods exhibit divergent biases for estimating regional racial composition. Third, the performance of all methods varies substantially across states. These results suggest that pre-trained machine learning models are preferable to BISG for individual classification. Furthermore, mixed results across states underscore the need for researchers to empirically validate their chosen race/ethnicity proxy in their populations of interest.