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In this paper, we provide a holistic analysis of the different sources ofbias, Upstream, Sample and Overampflication biases, in NLP models. Weinvestigate how they impact the fairness of the task of text classification. Wealso investigate the impact of removing these biases using different debiasingtechniques on the fairness of text classification. We found thatoveramplification bias is the most impactful bias on the fairness of textclassification. And that removing overamplification bias by fine-tuning the LMmodels on a dataset with balanced representations of the different identitygroups leads to fairer text classification models. Finally, we build on ourfindings and introduce practical guidelines on how to have a fairer textclassification model.

On Bias and Fairness in NLP: How to have a fairer text classification?