# Unsupervised Discovery of Implicit Gender Bias

arXiv: https://arxiv.org/abs/2004.08361 · HTML: https://mlsanity.com/paper/2004.08361/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2020.

## Built on by (most cited first, 30 of 34)

- [Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey](https://mlsanity.com/paper/2210.07700/) (2022)
- [Controlled Text Generation as Continuous Optimization with Multiple Constraints](https://mlsanity.com/paper/2108.01850/) (2021)
- [Gradient-Based Constrained Sampling from Language Models](https://mlsanity.com/paper/2205.12558/) (2022)
- [Uncovering Implicit Gender Bias in Narratives through Commonsense Inference](https://mlsanity.com/paper/2109.06437/) (2021)
- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [SODAPOP: Open-Ended Discovery of Social Biases in Social Commonsense Reasoning Models](https://mlsanity.com/paper/2210.07269/) (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)
- [Detecting Community Sensitive Norm Violations in Online Conversations](https://mlsanity.com/paper/2110.04419/) (2021)
- [Gendered Mental Health Stigma in Masked Language Models](https://mlsanity.com/paper/2210.15144/) (2022)
- [Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?](https://mlsanity.com/paper/2109.10052/) (2021)
- [Causal Intervention Improves Implicit Sentiment Analysis](https://mlsanity.com/paper/2208.09329/) (2022)
- [Improving Generalizability in Implicitly Abusive Language Detection with Concept Activation Vectors](https://mlsanity.com/paper/2204.02261/) (2022)
- [Style Pooling: Automatic Text Style Obfuscation for Improved Classification Fairness](https://mlsanity.com/paper/2109.04624/) (2021)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [mCSQA: Multilingual Commonsense Reasoning Dataset with Unified Creation Strategy by Language Models and Humans](https://mlsanity.com/paper/2406.04215/) (2024)
- [What's Not Said Still Hurts: A Description-Based Evaluation Framework for Measuring Social Bias in LLMs](https://mlsanity.com/paper/2502.19749/) (2025)
- [Trustworthy Social Bias Measurement](https://mlsanity.com/paper/2212.11672/) (2022)
- [Social Norm Bias: Residual Harms of Fairness-Aware Algorithms](https://mlsanity.com/paper/2108.11056/) (2021)
- [Quantifying Gender Biases Towards Politicians on Reddit](https://mlsanity.com/paper/2112.12014/) (2021)
- [Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts](https://mlsanity.com/paper/2304.12810/) (2023)
- [How Prevalent is Gender Bias in ChatGPT? -- Exploring German and English ChatGPT Responses](https://mlsanity.com/paper/2310.03031/) (2023)
- [Undesirable Biases in NLP: Addressing Challenges of Measurement](https://mlsanity.com/paper/2211.13709/) (2022)
- ["I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets](https://mlsanity.com/paper/2304.13557/) (2023)
- [TaCo: Targeted Concept Erasure Prevents Non-Linear Classifiers From Detecting Protected Attributes](https://mlsanity.com/paper/2312.06499/) (2023)
- [A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale](https://mlsanity.com/paper/2211.14358/) (2022)
- [TalkUp: Paving the Way for Understanding Empowering Language](https://mlsanity.com/paper/2305.14326/) (2023)
- [CO-STAR: Conceptualisation of Stereotypes for Analysis and Reasoning](https://mlsanity.com/paper/2112.00819/) (2021)
- [KTCR: Improving Implicit Hate Detection with Knowledge Transfer driven Concept Refinement](https://mlsanity.com/paper/2410.15314/) (2024)
- [Structured Reasoning for Fairness: A Multi-Agent Approach to Bias Detection in Textual Data](https://mlsanity.com/paper/2503.00355/) (2025)
- [Clinical Notes Reveal Physician Fatigue](https://mlsanity.com/paper/2312.03077/) (2023)

## Abstract (arXiv)

Despite their prevalence in society, social biases are difficult to identify, primarily because human judgements in this domain can be unreliable. We take an unsupervised approach to identifying gender bias against women at a comment level and present a model that can surface text likely to contain bias. Our main challenge is forcing the model to focus on signs of implicit bias, rather than other artifacts in the data. Thus, our methodology involves reducing the influence of confounds through propensity matching and adversarial learning. Our analysis shows how biased comments directed towards female politicians contain mixed criticisms, while comments directed towards other female public figures focus on appearance and sexualization. Ultimately, our work offers a way to capture subtle biases in various domains without relying on subjective human judgements.