In this paper, we advance the current state-of-the-art method for debiasing\nmonolingual word embeddings so as to generalize well in a multilingual setting.\nWe consider different methods to quantify bias and different debiasing\napproaches for monolingual as well as multilingual settings. We demonstrate the\nsignificance of our bias-mitigation approach on downstream NLP applications.\nOur proposed methods establish the state-of-the-art performance for debiasing\nmultilingual embeddings for three Indian languages - Hindi, Bengali, and Telugu\nin addition to English. We believe that our work will open up new opportunities\nin building unbiased downstream NLP applications that are inherently dependent\non the quality of the word embeddings used.\n