# Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia

arXiv: https://arxiv.org/abs/2106.01601 · HTML: https://mlsanity.com/paper/2106.01601/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## Builds on (most cited first, 8 of 8)

- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods](https://mlsanity.com/paper/1804.06876/) (2018)
- [Event2Mind: Commonsense Inference on Events, Intents, and Reactions](https://mlsanity.com/paper/1805.06939/) (2018)
- [Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction](https://mlsanity.com/paper/1909.05360/) (2019)
- [Quantifying Intimacy in Language](https://mlsanity.com/paper/2011.03020/) (2020)
- [EventPlus: A Temporal Event Understanding Pipeline](https://mlsanity.com/paper/2101.04922/) (2021)
- [Automated News Suggestions for Populating Wikipedia Entity Pages](https://mlsanity.com/paper/1703.10344/) (2017)

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

- ["Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters](https://mlsanity.com/paper/2310.09219/) (2023)
- [DEGREE: A Data-Efficient Generation-Based Event Extraction Model](https://mlsanity.com/paper/2108.12724/) (2021)
- [Gender Bias in Large Language Models across Multiple Languages](https://mlsanity.com/paper/2403.00277/) (2024)
- [A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models](https://mlsanity.com/paper/2202.08772/) (2022)
- [Twists, Humps, and Pebbles: Multilingual Speech Recognition Models Exhibit Gender Performance Gaps](https://mlsanity.com/paper/2402.17954/) (2024)
- [How Gender Debiasing Affects Internal Model Representations, and Why It Matters](https://mlsanity.com/paper/2204.06827/) (2022)
- [LLMs with Industrial Lens: Deciphering the Challenges and Prospects -- A Survey](https://mlsanity.com/paper/2402.14558/) (2024)
- [Wikibio: a Semantic Resource for the Intersectional Analysis of Biographical Events](https://mlsanity.com/paper/2306.09505/) (2023)
- [Implicit Bias in LLMs: A Survey](https://mlsanity.com/paper/2503.02776/) (2025)
- [Trustworthy Social Bias Measurement](https://mlsanity.com/paper/2212.11672/) (2022)
- [AI-generated stories favour stability over change: homogeneity and cultural stereotyping in narratives generated by gpt-4o-mini](https://mlsanity.com/paper/2507.22445/) (2025)
- [Investigating Annotator Bias in Large Language Models for Hate Speech Detection](https://mlsanity.com/paper/2406.11109/) (2024)
- [Unveiling and Mitigating Bias in Large Language Model Recommendations: A Path to Fairness](https://mlsanity.com/paper/2409.10825/) (2024)
- [EDIN: An End-to-end Benchmark and Pipeline for Unknown Entity Discovery and Indexing](https://mlsanity.com/paper/2205.12570/) (2022)
- [Social Bias in Popular Question-Answering Benchmarks](https://mlsanity.com/paper/2505.15553/) (2025)
- [Who Gets Which Message? Auditing Demographic Bias in LLM-Generated Targeted Text](https://mlsanity.com/paper/2601.17172/) (2026)
- [A Moral- and Event- Centric Inspection of Gender Bias in Fairy Tales at A Large Scale](https://mlsanity.com/paper/2211.14358/) (2022)
- [Are Fairy Tales Fair? Analyzing Gender Bias in Temporal Narrative Event Chains of Children's Fairy Tales](https://mlsanity.com/paper/2305.16641/) (2023)
- [A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement](https://mlsanity.com/paper/2404.04035/) (2024)
- [Guidelines and a Corpus for Extracting Biographical Events](https://mlsanity.com/paper/2206.03547/) (2022)
- [NECE: Narrative Event Chain Extraction Toolkit](https://mlsanity.com/paper/2208.08063/) (2022)
- [Diversity matters: Robustness of bias measurements in Wikidata](https://mlsanity.com/paper/2302.14027/) (2023)
- [Polar Ducks and Where to Find Them: Enhancing Entity Linking with Duck Typing and Polar Box Embeddings](https://mlsanity.com/paper/2305.12027/) (2023)
- [From Preferences to Prejudice: The Role of Alignment Tuning in Shaping Social Bias in Video Diffusion Models](https://mlsanity.com/paper/2510.17247/) (2025)
- [An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents](https://mlsanity.com/paper/2602.03775/) (2026)
- [Class Cardinality Comparison as a Fermi Problem](https://mlsanity.com/paper/2303.04532/) (2023)
- [MultiContrievers: Analysis of Dense Retrieval Representations](https://mlsanity.com/paper/2402.15925/) (2024)
- [Expert-Guided Extinction of Toxic Tokens for Debiased Generation](https://mlsanity.com/paper/2405.19299/) (2024)
- [Reflecting the Male Gaze: Quantifying Female Objectification in 19th and 20th Century Novels](https://mlsanity.com/paper/2403.17158/) (2024)
- [Unveiling Gender Bias in Large Language Models: Using Teacher's Evaluation in Higher Education As an Example](https://mlsanity.com/paper/2409.09652/) (2024)

## Abstract (arXiv)

Human activities can be seen as sequences of events, which are crucial to understanding societies. Disproportional event distribution for different demographic groups can manifest and amplify social stereotypes, and potentially jeopardize the ability of members in some groups to pursue certain goals. In this paper, we present the first event-centric study of gender biases in a Wikipedia corpus. To facilitate the study, we curate a corpus of career and personal life descriptions with demographic information consisting of 7,854 fragments from 10,412 celebrities. Then we detect events with a state-of-the-art event detection model, calibrate the results using strategically generated templates, and extract events that have asymmetric associations with genders. Our study discovers that the Wikipedia pages tend to intermingle personal life events with professional events for females but not for males, which calls for the awareness of the Wikipedia community to formalize guidelines and train the editors to mind the implicit biases that contributors carry. Our work also lays the foundation for future works on quantifying and discovering event biases at the corpus level.