  The concept of fairness is gaining popularity in academia and industry.
Social media is especially vulnerable to media biases and toxic language and
comments. We propose a fair ML pipeline that takes a text as input and
determines whether it contains biases and toxic content. Then, based on
pre-trained word embeddings, it suggests a set of new words by substituting the
bi-ased words, the idea is to lessen the effects of those biases by replacing
them with alternative words. We compare our approach to existing fairness
models to determine its effectiveness. The results show that our proposed
pipeline can de-tect, identify, and mitigate biases in social media data
