# Predicting Race and Ethnicity From the Sequence of Characters in a Name

arXiv: https://arxiv.org/abs/1805.02109 · HTML: https://mlsanity.com/paper/1805.02109/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 May 2018.

## Built on by (most cited first, 26 of 26)

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- [Using Embedding Models to Improve Probabilistic Race Prediction](https://mlsanity.com/paper/2604.22555/) (2026)
- [How should we proxy for race/ethnicity? Comparing Bayesian improved surname geocoding to machine learning methods](https://mlsanity.com/paper/2206.14583/) (2022)
- [Whose Name Comes Up? III: Persona Prompting Effects in LLM-Based Scholar Recommendation](https://mlsanity.com/paper/2605.28187/) (2026)
- [Rethnicity: Predicting Ethnicity from Names](https://mlsanity.com/paper/2109.09228/) (2021)
- [Instate: Predicting the State of Residence From Last Name](https://mlsanity.com/paper/2303.06823/) (2023)
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- [Predicting Star Scientists in the Field of Artificial Intelligence: A Machine Learning Approach](https://mlsanity.com/paper/2407.14559/) (2024)
- [Analyzing the Evolution of Graphs and Texts](https://mlsanity.com/paper/2411.06295/) (2024)
- [STRATA: A Name-and-Geography Race Inference Model for Fair Lending and Housing Equity Applications](https://mlsanity.com/paper/2504.21259/) (2025)
- [Can LLMs Predict Academic Collaboration? Topology Heuristics vs. LLM-Based Link Prediction on Real Co-authorship Networks](https://mlsanity.com/paper/2604.01379/) (2026)
- [NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data](https://mlsanity.com/paper/2604.10401/) (2026)
- [Participation and Representation in Local Government Speech](https://mlsanity.com/paper/2604.21202/) (2026)
- [War in the Abstract: The Rise and Consequences of Militarized Language in Scientific Communication](https://mlsanity.com/paper/2606.23462/) (2026)
- [Probabilistic Race and Ethnicity Prediction Using Group-Specific Name Lists](https://mlsanity.com/paper/2610.06273/) (2026)

## Abstract (arXiv)

To answer questions about racial inequality and fairness, we often need a way to infer race and ethnicity from names. One way to infer race and ethnicity from names is by relying on the Census Bureau's list of popular last names. The list, however, suffers from at least three limitations: 1. it only contains last names, 2. it only includes popular last names, and 3. it is updated once every 10 years. To provide better generalization, and higher accuracy when first names are available, we model the relationship between characters in a name and race and ethnicity using various techniques. A model using Long Short-Term Memory works best with out-of-sample accuracy of .85. The best-performing last-name model achieves out-of-sample accuracy of .81. To illustrate the utility of the models, we apply them to campaign finance data to estimate the share of donations made by people of various racial groups, and to news data to estimate the coverage of various races and ethnicities in the news.