# Mitigating Gender Bias in Machine Learning Data Sets

arXiv: https://arxiv.org/abs/2005.06898 · HTML: https://mlsanity.com/paper/2005.06898/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2020.

## Built on by (most cited first, 5 of 5)

- [Bias of AI-Generated Content: An Examination of News Produced by Large Language Models](https://mlsanity.com/paper/2309.09825/) (2023)
- [What Is The Political Content in LLMs' Pre- and Post-Training Data?](https://mlsanity.com/paper/2509.22367/) (2025)
- [Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts](https://mlsanity.com/paper/2304.12810/) (2023)
- [Assumed Identities: Quantifying Gender Bias in Machine Translation of Gender-Ambiguous Occupational Terms](https://mlsanity.com/paper/2503.04372/) (2025)
- [Local Contrastive Editing of Gender Stereotypes](https://mlsanity.com/paper/2410.17739/) (2024)

## Abstract (arXiv)

Artificial Intelligence has the capacity to amplify and perpetuate societal biases and presents profound ethical implications for society. Gender bias has been identified in the context of employment advertising and recruitment tools, due to their reliance on underlying language processing and recommendation algorithms. Attempts to address such issues have involved testing learned associations, integrating concepts of fairness to machine learning and performing more rigorous analysis of training data. Mitigating bias when algorithms are trained on textual data is particularly challenging given the complex way gender ideology is embedded in language. This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.