  Intersectional bias is a bias caused by an overlap of multiple social factors
like gender, sexuality, race, disability, religion, etc. A recent study has
shown that word embedding models can be laden with biases against
intersectional groups like African American females, etc. The first step
towards tackling such intersectional biases is to identify them. However,
discovering biases against different intersectional groups remains a
challenging task. In this work, we present WordBias, an interactive visual tool
designed to explore biases against intersectional groups encoded in static word
embeddings. Given a pretrained static word embedding, WordBias computes the
association of each word along different groups based on race, age, etc. and
then visualizes them using a novel interactive interface. Using a case study,
we demonstrate how WordBias can help uncover biases against intersectional
groups like Black Muslim Males, Poor Females, etc. encoded in word embedding.
In addition, we also evaluate our tool using qualitative feedback from expert
interviews. The source code for this tool can be publicly accessed for
reproducibility at github.com/bhavyaghai/WordBias.
