Evaluating Biased Attitude Associations of Language Models in an
  Intersectional Context
Shiva Omrani Sabbaghi Robert Wolfe Aylin Caliskan
Language models are trained on large-scale corpora that embed implicit biases
documented in psychology. Valence associations (pleasantness/unpleasantness) of
social groups determine the biased attitudes towards groups and concepts in
social cognition. Building on this established literature, we quantify how
social groups are valenced in English language models using a sentence template
that provides an intersectional context. We study biases related to age,
education, gender, height, intelligence, literacy, race, religion, sex, sexual
orientation, social class, and weight. We present a concept projection approach
to capture the valence subspace through contextualized word embeddings of
language models. Adapting the projection-based approach to embedding
association tests that quantify bias, we find that language models exhibit the
most biased attitudes against gender identity, social class, and sexual
orientation signals in language. We find that the largest and better-performing
model that we study is also more biased as it effectively captures bias
embedded in sociocultural data. We validate the bias evaluation method by
overperforming on an intrinsic valence evaluation task. The approach enables us
to measure complex intersectional biases as they are known to manifest in the
outputs and applications of language models that perpetuate historical biases.
Moreover, our approach contributes to design justice as it studies the
associations of groups underrepresented in language such as transgender and
homosexual individuals.