Debiasing by obfuscating with 007-classifiers promotes fairness in multi-community settings

Abstract

While there has been considerable amount of research on bias mitigation algorithms, two properties: multi-community perspective and fairness to *all* communities have not been given sufficient attention. Focusing on these, we propose an obfuscation based data augmentation debiasing approach. In it we add to the training data *obfuscated* versions of *all* false positive instances irrespective of source community. We test our approach by debiasing toxicity classifiers built using 5 neural models (multi layer perceptron model and masked language models) and 3 datasets in a 4 communities setting. We also explore 4 different obfuscators for debiasing. Results demonstrate the merits of our approach: bias is reduced for almost all of our runs without sacrificing false positive rates or F1 scores for minority or majority communities. In contrast, the 4 state of the art baselines typically make performance sacrifices (often large) while reducing bias. Crucially, we demonstrate that it is possible to debias while maintaining standards for both minority and majority communities.