  Word embeddings are the standard model for semantic and syntactic
representations of words. Unfortunately, these models have been shown to
exhibit undesirable word associations resulting from gender, racial, and
religious biases. Existing post-processing methods for debiasing word
embeddings are unable to mitigate gender bias hidden in the spatial arrangement
of word vectors. In this paper, we propose RAN-Debias, a novel gender debiasing
methodology which not only eliminates the bias present in a word vector but
also alters the spatial distribution of its neighbouring vectors, achieving a
bias-free setting while maintaining minimal semantic offset. We also propose a
new bias evaluation metric - Gender-based Illicit Proximity Estimate (GIPE),
which measures the extent of undue proximity in word vectors resulting from the
presence of gender-based predilections. Experiments based on a suite of
evaluation metrics show that RAN-Debias significantly outperforms the
state-of-the-art in reducing proximity bias (GIPE) by at least 42.02%. It also
reduces direct bias, adding minimal semantic disturbance, and achieves the best
performance in a downstream application task (coreference resolution).
