# Dialect and Gender Bias in YouTube's Spanish Captioning System

arXiv: https://arxiv.org/abs/2602.24002 · HTML: https://mlsanity.com/paper/2602.24002/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 48 references that are in the map.
- First posted on arXiv 27 Feb 2026.

## Builds on (most cited first, 7 of 7)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Quantifying Bias in Automatic Speech Recognition](https://mlsanity.com/paper/2103.15122/) (2021)
- [Gender Representation in French Broadcast Corpora and Its Impact on ASR Performance](https://mlsanity.com/paper/1908.08717/) (2019)
- [The IBM 2016 English Conversational Telephone Speech Recognition System](https://mlsanity.com/paper/1604.08242/) (2016)
- [Regionalized models for Spanish language variations based on Twitter](https://mlsanity.com/paper/2110.06128/) (2021)
- [Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning](https://mlsanity.com/paper/2205.13961/) (2022)
- [Pushing the performances of ASR models on English and Spanish accents](https://mlsanity.com/paper/2212.12048/) (2022)

## Abstract (arXiv)

Spanish is the official language of twenty-one countries and is spoken by over 441 million people. Naturally, there are many variations in how Spanish is spoken across these countries. Media platforms such as YouTube rely on automatic speech recognition systems to make their content accessible to different groups of users. However, YouTube offers only one option for automatically generating captions in Spanish. This raises the question: could this captioning system be biased against certain Spanish dialects? This study examines the potential biases in YouTube's automatic captioning system by analyzing its performance across various Spanish dialects. By comparing the quality of captions for female and male speakers from different regions, we identify systematic disparities which can be attributed to specific dialects. Our study provides further evidence that algorithmic technologies deployed on digital platforms need to be calibrated to the diverse needs and experiences of their user populations.