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Non-contextual word embedding models have been shown to inherit human-likestereotypical biases of gender, race and religion from the training corpora. Tocounter this issue, a large body of research has emerged which aims to mitigatethese biases while keeping the syntactic and semantic utility of embeddingsintact. This paper describes Fair Embedding Engine (FEE), a library foranalysing and mitigating gender bias in word embeddings. FEE combines variousstate of the art techniques for quantifying, visualising and mitigating genderbias in word embeddings under a standard abstraction. FEE will aidpractitioners in fast track analysis of existing debiasing methods on theirembedding models. Further, it will allow rapid prototyping of new methods byevaluating their performance on a suite of standard metrics.

Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings