Using Embedding Models to Improve Probabilistic Race Prediction
The eBISG method uses pre-trained text embeddings to create dense vectors for names, allowing it to estimate race probabilities for individuals with uncommon surnames not found in Census data. By replacing a generic prior with name-specific information, the system significantly improves predictive performance and reduces income-correlated bias.