# The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings

arXiv: https://arxiv.org/abs/2301.00792 · HTML: https://mlsanity.com/paper/2301.00792/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 2 Jan 2023.

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

- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Semantics derived automatically from language corpora contain human-like biases](https://mlsanity.com/paper/1608.07187/) (2016)
- [Word Embeddings Quantify 100 Years of Gender and Ethnic Stereotypes](https://mlsanity.com/paper/1711.08412/) (2017)
- [All-but-the-Top: Simple and Effective Postprocessing for Word Representations](https://mlsanity.com/paper/1702.01417/) (2017)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [FRAGE: Frequency-Agnostic Word Representation](https://mlsanity.com/paper/1809.06858/) (2018)
- [Measuring Model Biases in the Absence of Ground Truth](https://mlsanity.com/paper/2103.03417/) (2021)
- [Controlled Experiments for Word Embeddings](https://mlsanity.com/paper/1510.02675/) (2015)

## Built on by (most cited first, 2 of 2)

- [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://mlsanity.com/paper/2306.08158/) (2023)
- [Unraveling Downstream Gender Bias from Large Language Models: A Study on AI Educational Writing Assistance](https://mlsanity.com/paper/2311.03311/) (2023)

## Abstract (arXiv)

Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word embedding-based bias quantification methods. We find that Skip-gram with negative sampling and GloVe tend to detect male bias in high frequency words, while GloVe tends to return female bias in low frequency words. We show these behaviors still exist when words are randomly shuffled. This proves that the frequency-based effect observed in unshuffled corpora stems from properties of the metric rather than from word associations. The effect is spurious and problematic since bias metrics should depend exclusively on word co-occurrences and not individual word frequencies. Finally, we compare these results with the ones obtained with an alternative metric based on Pointwise Mutual Information. We find that this metric does not show a clear dependence on frequency, even though it is slightly skewed towards male bias across all frequencies.