  The widespread use of ML-based decision making in domains with high societal
impact such as recidivism, job hiring and loan credit has raised a lot of
concerns regarding potential discrimination. In particular, in certain cases it
has been observed that ML algorithms can provide different decisions based on
sensitive attributes such as gender or race and therefore can lead to
discrimination. Although, several fairness-aware ML approaches have been
proposed, their focus has been largely on preserving the overall classification
accuracy while improving fairness in predictions for both protected and
non-protected groups (defined based on the sensitive attribute(s)). The overall
accuracy however is not a good indicator of performance in case of class
imbalance, as it is biased towards the majority class. As we will see in our
experiments, many of the fairness-related datasets suffer from class imbalance
and therefore, tackling fairness requires also tackling the imbalance problem.
  To this end, we propose AdaFair, a fairness-aware classifier based on
AdaBoost that further updates the weights of the instances in each boosting
round taking into account a cumulative notion of fairness based upon all
current ensemble members, while explicitly tackling class-imbalance by
optimizing the number of ensemble members for balanced classification error.
Our experiments show that our approach can achieve parity in true positive and
true negative rates for both protected and non-protected groups, while it
significantly outperforms existing fairness-aware methods up to 25% in terms of
balanced error.
