Bias in word embeddings is often measured using bipolar dimensions, constructed as the difference between two anchor centroids. This technique assumes both poles are symmetrical and equally informative. However, normativity literature shows that one category may function as the unmarked norm, with others framed as marked deviations. In race, whiteness typically holds the normative position, and embedding-based race dimensions may inherit the skew. We test this possibility using dimensions constructed from validated African–European name anchors, probed with neutral and valence words. In three embedding models (Wiki-News, South African news, Google News), we assess whether race dimensions favour whiteness as a normative anchor, whether this skew is stronger in culturally specific models (SA, Google), and whether bipolar offsets amplify one pole, given unipolar evidence. Results show that neutral and valence terms cluster nearer to the white pole (most strongly in the Wiki-News model), indicating whiteness as the semantic default. Overshoot favoured Black in Google and Wiki-News, while White overshoot only occurred in the South African model. We argue that this captures racialised variance where the pole with more spread tends to exert greater leverage on the bipolar axis. The study provides quantitative evidence of white-normative anchoring and diagnostics for asymmetric amplification in embedding-based bias measures.
Ohamadike et al. (Mon,) studied this question.