# Assessing the quality and coherence of word embeddings after SCM-based intersectional bias mitigation

arXiv: https://arxiv.org/abs/2601.04393 · HTML: https://mlsanity.com/paper/2601.04393/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings](https://mlsanity.com/paper/1607.06520/) (2016)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [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)
- [Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://mlsanity.com/paper/1904.04047/) (2019)
- [Attenuating Bias in Word Vectors](https://mlsanity.com/paper/1901.07656/) (2019)
- [Obtaining Better Static Word Embeddings Using Contextual Embedding Models](https://mlsanity.com/paper/2106.04302/) (2021)
- [Stepmothers are mean and academics are pretentious: What do pretrained language models learn about you?](https://mlsanity.com/paper/2109.10052/) (2021)
- [Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis](https://mlsanity.com/paper/2011.12086/) (2020)
- [WordBias: An Interactive Visual Tool for Discovering Intersectional Biases Encoded in Word Embeddings](https://mlsanity.com/paper/2103.03598/) (2021)

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

- [Causally Disentangled Contrastive Learning for Multilingual Speaker Embeddings](https://mlsanity.com/paper/2602.01363/) (2026)
- [SCM-based Fairness and Faithful Explainability for Legal Document Classification](https://mlsanity.com/paper/2610.00045/) (2026)

## Abstract (arXiv)

Static word embeddings often absorb social biases from the text they learn from, and those biases can quietly shape downstream systems. Prior work that uses the Stereotype Content Model (SCM) has focused mostly on single-group bias along warmth and competence. We broaden that lens to intersectional bias by building compound representations for pairs of social identities through summation or concatenation, and by applying three debiasing strategies: Subtraction, Linear Projection, and Partial Projection. We study three widely used embedding families (Word2Vec, GloVe, and ConceptNet Numberbatch) and assess them with two complementary views of utility: whether local neighborhoods remain coherent and whether analogy behavior is preserved. Across models, SCM-based mitigation carries over well to the intersectional case and largely keeps the overall semantic landscape intact. The main cost is a familiar trade off: methods that most tightly preserve geometry tend to be more cautious about analogy behavior, while more assertive projections can improve analogies at the expense of strict neighborhood stability. Partial Projection is reliably conservative and keeps representations steady; Linear Projection can be more assertive; Subtraction is a simple baseline that remains competitive. The choice between summation and concatenation depends on the embedding family and the application goal. Together, these findings suggest that intersectional debiasing with SCM is practical in static embeddings, and they offer guidance for selecting aggregation and debiasing settings when balancing stability against analogy performance.