Using Embedding Models to Improve Probabilistic Race Prediction
Estimating racial disparity requires individual-level race data, which are often unavailable due to the sensitivity of collecting such information. To address this problem, many researchers utilize Bayesian Improved Surname Geocoding (BISG), which have critically relied on Census surname data. Unfortunately, these data capture race-surname relationships only for common surnames, omitting approximately 10% of the US population. We show that predictive performance degrades substantially for individuals with such omitted, uncommon surnames because standard BISG implementation relies on a uninformative generic prior in these cases. To address this limitation, we propose embedding-powered BISG...
| Authors | Noah Dasanaike, Kosuke Imai |
|---|---|
| Primary category | cs.CL |
| Published | 24 April 2026 |
| Last updated at source | 28 April 2026 |
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