# Decoding Demographic un-fairness from Indian Names

arXiv: https://arxiv.org/abs/2209.03089 · HTML: https://mlsanity.com/paper/2209.03089/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 14 references that are in the map.
- First posted on arXiv 7 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Predicting Race and Ethnicity From the Sequence of Characters in a Name](https://mlsanity.com/paper/1805.02109/) (2018)
- [Aspect-based Sentiment Analysis of Scientific Reviews](https://mlsanity.com/paper/2006.03257/) (2020)
- [What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Models](https://mlsanity.com/paper/2102.03692/) (2021)
- [raceBERT -- A Transformer-based Model for Predicting Race and Ethnicity from Names](https://mlsanity.com/paper/2112.03807/) (2021)
- [CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection](https://mlsanity.com/paper/2204.06389/) (2022)
- [Gender Inference using Statistical Name Characteristics in Twitter](https://mlsanity.com/paper/1606.05467/) (2016)
- [(Im)balance in the Representation of News? An Extensive Study on a Decade Long Dataset from India](https://mlsanity.com/paper/2110.14183/) (2021)
- [What's Kooking? Characterizing India's Emerging Social Network, Koo](https://mlsanity.com/paper/2103.13239/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Deep Dive into the Disparity of Word Error Rates Across Thousands of NPTEL MOOC Videos](https://mlsanity.com/paper/2307.10587/) (2023)
- [Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction](https://mlsanity.com/paper/2209.10292/) (2022)

## Abstract (arXiv)

Demographic classification is essential in fairness assessment in recommender systems or in measuring unintended bias in online networks and voting systems. Important fields like education and politics, which often lay a foundation for the future of equality in society, need scrutiny to design policies that can better foster equality in resource distribution constrained by the unbalanced demographic distribution of people in the country. We collect three publicly available datasets to train state-of-the-art classifiers in the domain of gender and caste classification. We train the models in the Indian context, where the same name can have different styling conventions (Jolly Abraham/Kumar Abhishikta in one state may be written as Abraham Jolly/Abishikta Kumar in the other). Finally, we also perform cross-testing (training and testing on different datasets) to understand the efficacy of the above models. We also perform an error analysis of the prediction models. Finally, we attempt to assess the bias in the existing Indian system as case studies and find some intriguing patterns manifesting in the complex demographic layout of the sub-continent across the dimensions of gender and caste.