# Debiasing Embeddings for Reduced Gender Bias in Text Classification

arXiv: https://arxiv.org/abs/1908.02810 · HTML: https://mlsanity.com/paper/1908.02810/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2019.

## Built on by (most cited first, 19 of 19)

- [A Survey on Gender Bias in Natural Language Processing](https://mlsanity.com/paper/2112.14168/) (2021)
- [Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics](https://mlsanity.com/paper/2106.14574/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Choose Your Lenses: Flaws in Gender Bias Evaluation](https://mlsanity.com/paper/2210.11471/) (2022)
- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [Domain Adaptation meets Individual Fairness. And they get along](https://mlsanity.com/paper/2205.00504/) (2022)
- [Individually Fair Gradient Boosting](https://mlsanity.com/paper/2103.16785/) (2021)
- [How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification](https://mlsanity.com/paper/2301.12855/) (2023)
- [In the Name of Fairness: Assessing the Bias in Clinical Record De-identification](https://mlsanity.com/paper/2305.11348/) (2023)
- [What Changed? Investigating Debiasing Methods using Causal Mediation Analysis](https://mlsanity.com/paper/2206.00701/) (2022)
- [Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairness](https://mlsanity.com/paper/2303.00673/) (2023)
- [Toward Understanding Bias Correlations for Mitigation in NLP](https://mlsanity.com/paper/2205.12391/) (2022)
- [GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in Explanations](https://mlsanity.com/paper/2406.11547/) (2024)
- [Counterfactual Multi-Token Fairness in Text Classification](https://mlsanity.com/paper/2202.03792/) (2022)
- [Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models](https://mlsanity.com/paper/2212.08399/) (2022)
- [A methodology to characterize bias and harmful stereotypes in natural language processing in Latin America](https://mlsanity.com/paper/2207.06591/) (2022)
- [Do Not Harm Protected Groups in Debiasing Language Representation Models](https://mlsanity.com/paper/2310.18458/) (2023)
- [Revisiting The Classics: A Study on Identifying and Rectifying Gender Stereotypes in Rhymes and Poems](https://mlsanity.com/paper/2403.11752/) (2024)
- [On Debiasing Text Embeddings Through Context Injection](https://mlsanity.com/paper/2410.12874/) (2024)

## Abstract (arXiv)

(Bolukbasi et al., 2016) demonstrated that pretrained word embeddings can inherit gender bias from the data they were trained on. We investigate how this bias affects downstream classification tasks, using the case study of occupation classification (De-Arteaga et al.,2019). We show that traditional techniques for debiasing embeddings can actually worsen the bias of the downstream classifier by providing a less noisy channel for communicating gender information. With a relatively minor adjustment, however, we show how these same techniques can be used to simultaneously reduce bias and maintain high classification accuracy.