# Multi-Dimensional Gender Bias Classification

arXiv: https://arxiv.org/abs/2005.00614 · HTML: https://mlsanity.com/paper/2005.00614/

## Key facts

- Cited by 80 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 May 2020.

## Built on by (most cited first, 30 of 80)

- [Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models](https://mlsanity.com/paper/2305.18189/) (2023)
- [Societal Biases in Language Generation: Progress and Challenges](https://mlsanity.com/paper/2105.04054/) (2021)
- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://mlsanity.com/paper/2208.03188/) (2022)
- ["I'm sorry to hear that": Finding New Biases in Language Models with a Holistic Descriptor Dataset](https://mlsanity.com/paper/2205.09209/) (2022)
- [RedditBias: A Real-World Resource for Bias Evaluation and Debiasing of Conversational Language Models](https://mlsanity.com/paper/2106.03521/) (2021)
- [Gender Bias in Machine Translation](https://mlsanity.com/paper/2104.06001/) (2021)
- [An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models](https://mlsanity.com/paper/2110.08527/) (2021)
- [Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling](https://mlsanity.com/paper/2107.03451/) (2021)
- [Theories of "Gender" in NLP Bias Research](https://mlsanity.com/paper/2205.02526/) (2022)
- [Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking](https://mlsanity.com/paper/2106.06052/) (2021)
- [Perturbation Augmentation for Fairer NLP](https://mlsanity.com/paper/2205.12586/) (2022)
- [Activation Oracles: Training and Evaluating LLMs as General-Purpose Activation Explainers](https://mlsanity.com/paper/2512.15674/) (2025)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution](https://mlsanity.com/paper/2403.03121/) (2024)
- [Uncovering Implicit Gender Bias in Narratives through Commonsense Inference](https://mlsanity.com/paper/2109.06437/) (2021)
- [Uncovering and Quantifying Social Biases in Code Generation](https://mlsanity.com/paper/2305.15377/) (2023)
- [Zero-Shot Robustification of Zero-Shot Models](https://mlsanity.com/paper/2309.04344/) (2023)
- [Hi, my name is Martha: Using names to measure and mitigate bias in generative dialogue models](https://mlsanity.com/paper/2109.03300/) (2021)
- [A Word on Machine Ethics: A Response to Jiang et al. (2021)](https://mlsanity.com/paper/2111.04158/) (2021)
- [Rissanen Data Analysis: Examining Dataset Characteristics via Description Length](https://mlsanity.com/paper/2103.03872/) (2021)
- [Deep Learning on a Healthy Data Diet: Finding Important Examples for Fairness](https://mlsanity.com/paper/2211.11109/) (2022)
- [Gender Bias in BERT -- Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task](https://mlsanity.com/paper/2306.15298/) (2023)
- [Investigating Failures of Automatic Translation in the Case of Unambiguous Gender](https://mlsanity.com/paper/2104.07838/) (2021)
- [Measuring Fairness with Biased Rulers: A Survey on Quantifying Biases in Pretrained Language Models](https://mlsanity.com/paper/2112.07447/) (2021)
- [Cultural Commonsense Knowledge for Intercultural Dialogues](https://mlsanity.com/paper/2402.10689/) (2024)
- [SANER: Annotation-free Societal Attribute Neutralizer for Debiasing CLIP](https://mlsanity.com/paper/2408.10202/) (2024)
- [Bias in News Summarization: Measures, Pitfalls and Corpora](https://mlsanity.com/paper/2309.08047/) (2023)
- [Quantifying Gender Bias Towards Politicians in Cross-Lingual Language Models](https://mlsanity.com/paper/2104.07505/) (2021)
- [Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance](https://mlsanity.com/paper/2311.03311/) (2023)
- [Gender Bias in Decision-Making with Large Language Models: A Study of Relationship Conflicts](https://mlsanity.com/paper/2410.11084/) (2024)

## Abstract (arXiv)

Machine learning models are trained to find patterns in data. NLP models can inadvertently learn socially undesirable patterns when training on gender biased text. In this work, we propose a general framework that decomposes gender bias in text along several pragmatic and semantic dimensions: bias from the gender of the person being spoken about, bias from the gender of the person being spoken to, and bias from the gender of the speaker. Using this fine-grained framework, we automatically annotate eight large scale datasets with gender information. In addition, we collect a novel, crowdsourced evaluation benchmark of utterance-level gender rewrites. Distinguishing between gender bias along multiple dimensions is important, as it enables us to train finer-grained gender bias classifiers. We show our classifiers prove valuable for a variety of important applications, such as controlling for gender bias in generative models, detecting gender bias in arbitrary text, and shed light on offensive language in terms of genderedness.