  Word vector embeddings have been shown to contain and amplify biases in data
they are extracted from. Consequently, many techniques have been proposed to
identify, mitigate, and attenuate these biases in word representations. In this
paper, we utilize interactive visualization to increase the interpretability
and accessibility of a collection of state-of-the-art debiasing techniques. To
aid this, we present Visualization of Embedding Representations for deBiasing
system ("VERB"), an open-source web-based visualization tool that helps the
users gain a technical understanding and visual intuition of the inner workings
of debiasing techniques, with a focus on their geometric properties. In
particular, VERB offers easy-to-follow use cases in exploring the effects of
these debiasing techniques on the geometry of high-dimensional word vectors. To
help understand how various debiasing techniques change the underlying
geometry, VERB decomposes each technique into interpretable sequences of
primitive transformations and highlights their effect on the word vectors using
dimensionality reduction and interactive visual exploration. VERB is designed
to target natural language processing (NLP) practitioners who are designing
decision-making systems on top of word embeddings, and also researchers working
with fairness and ethics of machine learning systems in NLP. It can also serve
as a visual medium for education, which helps an NLP novice to understand and
mitigate biases in word embeddings.
