# Can We Trust Race Prediction?

arXiv: https://arxiv.org/abs/2307.08496 · HTML: https://mlsanity.com/paper/2307.08496/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [Predicting Race and Ethnicity From the Sequence of Characters in a Name](https://mlsanity.com/paper/1805.02109/) (2018)
- [What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models](https://mlsanity.com/paper/2102.03692/) (2021)

## Abstract (arXiv)

In the absence of sensitive race and ethnicity data, researchers, regulators, and firms alike turn to proxies. In this paper, I train a Bidirectional Long Short-Term Memory (BiLSTM) model on a novel dataset of voter registration data from all 50 US states and create an ensemble that achieves up to 36.8% higher out of sample (OOS) F1 scores than the best performing machine learning models in the literature. Additionally, I construct the most comprehensive database of first and surname distributions in the US in order to improve the coverage and accuracy of Bayesian Improved Surname Geocoding (BISG) and Bayesian Improved Firstname Surname Geocoding (BIFSG). Finally, I provide the first high-quality benchmark dataset in order to fairly compare existing models and aid future model developers.