  Increasingly, software is making autonomous decisions in case of criminal
sentencing, approving credit cards, hiring employees, and so on. Some of these
decisions show bias and adversely affect certain social groups (e.g. those
defined by sex, race, age, marital status). Many prior works on bias mitigation
take the following form: change the data or learners in multiple ways, then see
if any of that improves fairness. Perhaps a better approach is to postulate
root causes of bias and then applying some resolution strategy. This paper
postulates that the root causes of bias are the prior decisions that affect-
(a) what data was selected and (b) the labels assigned to those examples. Our
Fair-SMOTE algorithm removes biased labels; and rebalances internal
distributions such that based on sensitive attribute, examples are equal in
both positive and negative classes. On testing, it was seen that this method
was just as effective at reducing bias as prior approaches. Further, models
generated via Fair-SMOTE achieve higher performance (measured in terms of
recall and F1) than other state-of-the-art fairness improvement algorithms. To
the best of our knowledge, measured in terms of number of analyzed learners and
datasets, this study is one of the largest studies on bias mitigation yet
presented in the literature.
