# What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models

arXiv: https://arxiv.org/abs/2102.03692 · HTML: https://mlsanity.com/paper/2102.03692/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 42 references that are in the map.
- First posted on arXiv 7 Feb 2021.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Beyond Parity: Fairness Objectives for Collaborative Filtering](https://mlsanity.com/paper/1705.08804/) (2017)
- [Using Image Fairness Representations in Diversity-Based Re-ranking for Recommendations](https://mlsanity.com/paper/1809.03577/) (2018)
- [Nationality Classification Using Name Embeddings](https://mlsanity.com/paper/1708.07903/) (2017)
- [Gender Inference using Statistical Name Characteristics in Twitter](https://mlsanity.com/paper/1606.05467/) (2016)
- [Inferring gender of a Twitter user using celebrities it follows](https://mlsanity.com/paper/1405.6667/) (2014)
- [Gender and Interest Targeting for Sponsored Post Advertising at Tumblr](https://mlsanity.com/paper/1606.07189/) (2016)

## Built on by (most cited first, 12 of 12)

- [Beyond Binary Gender Labels: Revealing Gender Biases in LLMs through Gender-Neutral Name Predictions](https://mlsanity.com/paper/2407.05271/) (2024)
- [Evaluating LLMs for Gender Disparities in Notable Persons](https://mlsanity.com/paper/2403.09148/) (2024)
- [Decoding Demographic un-fairness from Indian Names](https://mlsanity.com/paper/2209.03089/) (2022)
- [Gender inference: can chatGPT outperform common commercial tools?](https://mlsanity.com/paper/2312.00805/) (2023)
- [For the Underrepresented in Gender Bias Research: Chinese Name Gender Prediction with Heterogeneous Graph Attention Network](https://mlsanity.com/paper/2302.00419/) (2023)
- [Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names](https://mlsanity.com/paper/2311.11001/) (2023)
- [Predicting Gender by First Name Using Character-level Machine Learning](https://mlsanity.com/paper/2106.10156/) (2021)
- [Instate: Predicting the State of Residence From Last Name](https://mlsanity.com/paper/2303.06823/) (2023)
- [Can We Trust Race Prediction?](https://mlsanity.com/paper/2307.08496/) (2023)
- [For the Misgendered Chinese in Gender Bias Research: Multi-Task Learning with Knowledge Distillation for Pinyin Name-Gender Prediction](https://mlsanity.com/paper/2405.06221/) (2024)
- [Measuring and Analyzing Subjective Uncertainty in Scientific Communications](https://mlsanity.com/paper/2503.21114/) (2025)
- [Agentic Username Suggestion and Multimodal Gender Detection in Online Platforms: Introducing the PNGT-26K Dataset](https://mlsanity.com/paper/2509.11136/) (2025)

## Abstract (arXiv)

Gender information is no longer a mandatory input when registering for an account at many leading Internet companies. However, prediction of demographic information such as gender and age remains an important task, especially in intervention of unintentional gender/age bias in recommender systems. Therefore it is necessary to infer the gender of those users who did not to provide this information during registration. We consider the problem of predicting the gender of registered users based on their declared name. By analyzing the first names of 100M+ users, we found that genders can be very effectively classified using the composition of the name strings. We propose a number of character based machine learning models, and demonstrate that our models are able to infer the gender of users with much higher accuracy than baseline models. Moreover, we show that using the last names in addition to the first names improves classification performance further.