# Gender Representation and Bias in Indian Civil Service Mock Interviews

arXiv: https://arxiv.org/abs/2409.12194 · HTML: https://mlsanity.com/paper/2409.12194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 18 Sep 2024.

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

- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://mlsanity.com/paper/2004.09813/) (2020)
- [Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them](https://mlsanity.com/paper/1903.03862/) (2019)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021)
- [ChatGPT Perpetuates Gender Bias in Machine Translation and Ignores Non-Gendered Pronouns: Findings across Bengali and Five other Low-Resource Languages](https://mlsanity.com/paper/2305.10510/) (2023)
- [Analyzing Gender Bias within Narrative Tropes](https://mlsanity.com/paper/2011.00092/) (2020)
- [Are Explainability Tools Gender Biased? A Case Study on Face Presentation Attack Detection](https://mlsanity.com/paper/2304.13419/) (2023)
- [Disentangling Societal Inequality from Model Biases: Gender Inequality in Divorce Court Proceedings](https://mlsanity.com/paper/2307.10200/) (2023)

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

- [ParamBench: A Graduate-Level Benchmark for Evaluating LLM Understanding on Indic Subjects](https://mlsanity.com/paper/2508.16185/) (2025)
- [Two Centuries of Sexism in British Parliament: A Computational Analysis of Women's Representation in the Hansard Corpus](https://mlsanity.com/paper/2608.30485/) (2026)

## Abstract (arXiv)

This paper makes three key contributions. First, via a substantial corpus of 51,278 interview questions sourced from 888 YouTube videos of mock interviews of Indian civil service candidates, we demonstrate stark gender bias in the broad nature of questions asked to male and female candidates. Second, our experiments with large language models show a strong presence of gender bias in explanations provided by the LLMs on the gender inference task. Finally, we present a novel dataset of 51,278 interview questions that can inform future social science studies.