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

TL;DR

This paper evaluates intersectional bias mitigation in static word embeddings using SCM-based methods, analyzing their impact on semantic coherence and analogy tasks across different models.

Contribution

It extends SCM-based bias mitigation to intersectional identities and compares three debiasing strategies across multiple embedding models.

Findings

SCM-based mitigation effectively reduces intersectional bias.

Trade-off observed between neighborhood coherence and analogy performance.

Partial Projection offers a conservative and stable debiasing approach.

Abstract

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.