  Non-contextual word embedding models have been shown to inherit human-like
stereotypical biases of gender, race and religion from the training corpora. To
counter this issue, a large body of research has emerged which aims to mitigate
these biases while keeping the syntactic and semantic utility of embeddings
intact. This paper describes Fair Embedding Engine (FEE), a library for
analysing and mitigating gender bias in word embeddings. FEE combines various
state of the art techniques for quantifying, visualising and mitigating gender
bias in word embeddings under a standard abstraction. FEE will aid
practitioners in fast track analysis of existing debiasing methods on their
embedding models. Further, it will allow rapid prototyping of new methods by
evaluating their performance on a suite of standard metrics.
