# How Far Can It Go?: On Intrinsic Gender Bias Mitigation for Text Classification arXiv: https://arxiv.org/abs/2301.12855 ยท HTML: https://mlsanity.com/paper/2301.12855/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Builds on 26 of its 35 references that are in the map. - First posted on arXiv 30 Jan 2023. ## Builds on (most cited first, 26 of 26) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [Neural Machine Translation of Rare Words with Subword Units](https://mlsanity.com/paper/1508.07909/) (2015) - [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019) - [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016) - [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016) - [Language (Technology) is Power: A Critical Survey of "Bias" in NLP](https://mlsanity.com/paper/2005.14050/) (2020) - [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016) - [Counterfactual Fairness](https://mlsanity.com/paper/1703.06856/) (2017) - [On Measuring Social Biases in Sentence Encoders](https://mlsanity.com/paper/1903.10561/) (2019) - [Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting](https://mlsanity.com/paper/1901.09451/) (2019) - [Measuring Bias in Contextualized Word Representations](https://mlsanity.com/paper/1906.07337/) (2019) - [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) - [Information-Theoretic Probing with Minimum Description Length](https://mlsanity.com/paper/2003.12298/) (2020) - [Measuring and Reducing Gendered Correlations in Pre-trained Models](https://mlsanity.com/paper/2010.06032/) (2020) - [Gender Bias in Neural Natural Language Processing](https://mlsanity.com/paper/1807.11714/) (2018) - [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018) - [Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology](https://mlsanity.com/paper/1906.04571/) (2019) - [Towards Debiasing Sentence Representations](https://mlsanity.com/paper/2007.08100/) (2020) - [Reducing Gender Bias in Abusive Language Detection](https://mlsanity.com/paper/1808.07231/) (2018) - [Assessing Social and Intersectional Biases in Contextualized Word Representations](https://mlsanity.com/paper/1911.01485/) (2019) - [Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies](https://mlsanity.com/paper/2108.12084/) (2021) - [Debiasing Pre-trained Contextualised Embeddings](https://mlsanity.com/paper/2101.09523/) (2021) - [It's All in the Name: Mitigating Gender Bias with Name-Based Counterfactual Data Substitution](https://mlsanity.com/paper/1909.00871/) (2019) - [Debiasing Embeddings for Reduced Gender Bias in Text Classification](https://mlsanity.com/paper/1908.02810/) (2019) - [How Gender Debiasing Affects Internal Model Representations, and Why It Matters](https://mlsanity.com/paper/2204.06827/) (2022) ## Built on by (most cited first, 10 of 10) - [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023) - [NBIAS: A Natural Language Processing Framework for Bias Identification in Text](https://mlsanity.com/paper/2308.01681/) (2023) - [Towards Trustworthy AI: A Review of Ethical and Robust Large Language Models](https://mlsanity.com/paper/2407.13934/) (2024) - [Addressing Bias in Generative AI: Challenges and Research Opportunities in Information Management](https://mlsanity.com/paper/2502.10407/) (2025) - [Fair Text Classification with Wasserstein Independence](https://mlsanity.com/paper/2311.12689/) (2023) - [REFINE-LM: Mitigating Language Model Stereotypes via Reinforcement Learning](https://mlsanity.com/paper/2408.09489/) (2024) - [Model-based Counterfactual Generator for Gender Bias Mitigation](https://mlsanity.com/paper/2311.03186/) (2023) - [Responsible AI in NLP: GUS-Net Span-Level Bias Detection Dataset and Benchmark for Generalizations, Unfairness, and Stereotypes](https://mlsanity.com/paper/2410.08388/) (2024) - [Mitigating Extrinsic Gender Bias for Bangla Classification Tasks](https://mlsanity.com/paper/2411.10636/) (2024) - [Textual Data Bias Detection and Mitigation -- An Extensible Pipeline with Experimental Evaluation](https://mlsanity.com/paper/2512.10734/) (2025) ## Abstract (arXiv) To mitigate gender bias in contextualized language models, different intrinsic mitigation strategies have been proposed, alongside many bias metrics. Considering that the end use of these language models is for downstream tasks like text classification, it is important to understand how these intrinsic bias mitigation strategies actually translate to fairness in downstream tasks and the extent of this. In this work, we design a probe to investigate the effects that some of the major intrinsic gender bias mitigation strategies have on downstream text classification tasks. We discover that instead of resolving gender bias, intrinsic mitigation techniques and metrics are able to hide it in such a way that significant gender information is retained in the embeddings. Furthermore, we show that each mitigation technique is able to hide the bias from some of the intrinsic bias measures but not all, and each intrinsic bias measure can be fooled by some mitigation techniques, but not all. We confirm experimentally, that none of the intrinsic mitigation techniques used without any other fairness intervention is able to consistently impact extrinsic bias. We recommend that intrinsic bias mitigation techniques should be combined with other fairness interventions for downstream tasks.