LSTM+Geo with xgBoost Filtering: A Novel Approach for Race and Ethnicity Imputation with Reduced Bias
Accurate imputation of race and ethnicity (R&E) is crucial for analyzing disparities and informing policy. Methods like Bayesian Improved Surname Geocoding (BISG) are widely used but exhibit limitations, including systematic misclassification biases linked to socioeconomic status. This paper introduces LSTM+Geo, a novel approach enhancing Long Short-Term Memory (LSTM) networks with census tract geolocation information. Using a large voter dataset, we demonstrate that LSTM+Geo (88.7% accuracy) significantly outperforms standalone LSTM (86.4%) and Bayesian methods like BISG (82.9%) and BIFSG (86.8%) in accuracy and F1-score on a held-out validation set. LSTM+Geo reduces the rate at which non-White individuals are misclassified as White (White FPR 19.3%) compared to name-only LSTMs (White FPR 24.6%). While sophisticated ensemble methods incorporating XGBoost achieve the highest overall accuracy (up to 89.4%) and lowest White FPR (17.8%), LSTM+Geo offers strong standalone performance with improved bias characteristics compared to baseline models. Integrating LSTM+Geo into an XGBoost ensemble further boosts accuracy, highlighting its utility as both a standalone model and a component for advanced systems. We give a caution at the end regarding the appropriate use of these methods.
Code (0)
등록된 구현이 없습니다.
Tasks
ImputationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
NY Real Estate Racial Equity Analysis via Applied Machine Learning
This study analyzes tract-level real estate ownership patterns in New York State (NYS) and New York City (NYC) to uncover racial disparities. We use an advanced race/ethnicity imputation model (LSTM+Geo with XGBoost filt…
ImputationRIDDLE: Race and ethnicity Imputation from Disease history with Deep LEarning
Anonymized electronic medical records are an increasingly popular source of research data. However, these datasets often lack race and ethnicity information. This creates problems for researchers modeling human disease, …
ImputationRace and ethnicity data for first, middle, and last names
We provide the largest compiled publicly available dictionaries of first, middle, and last names for the purpose of imputing race and ethnicity using, for example, Bayesian Improved Surname Geocoding (BISG). The dictiona…
ImputationBenchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMark
Plasma diagnostic models for tokamak fusion devices are almost universally evaluated on clean, complete sensor data. In practice, fusion diagnostics fail regularly: acquisition systems start late, individual sensors die,…
raceBERT -- A Transformer-based Model for Predicting Race and Ethnicity from Names
This paper presents raceBERT -- a transformer-based model for predicting race and ethnicity from character sequences in names, and an accompanying python package. Using a transformer-based model trained on a U.S. Florida…