Fair Embedding Engine: A Library for Analyzing and Mitigating Gender Bias in Word Embeddings
This addresses the problem of gender bias in word embeddings for NLP practitioners, but it is incremental as it packages existing methods into a library.
The paper introduces Fair Embedding Engine (FEE), a library that combines state-of-the-art techniques for analyzing and mitigating gender bias in word embeddings, enabling practitioners to quickly evaluate debiasing methods and prototype new ones.
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.