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We present a new approach for detecting human-like social biases in wordembeddings using representational similarity analysis. Specifically, we probecontextualized and non-contextualized embeddings for evidence of intersectionalbiases against Black women. We show that these embeddings represent Black womenas simultaneously less feminine than White women, and less Black than Blackmen. This finding aligns with intersectionality theory, which argues thatmultiple identity categories (such as race or sex) layer on top of each otherin order to create unique modes of discrimination that are not shared by anyindividual category.

Unequal Representations: Analyzing Intersectional Biases in Word Embeddings Using Representational Similarity Analysis