  Data-driven AI systems can lead to discrimination on the basis of protected
attributes like gender or race. One reason for this behavior is the encoded
societal biases in the training data (e.g., females are underrepresented),
which is aggravated in the presence of unbalanced class distributions (e.g.,
"granted" is the minority class). State-of-the-art fairness-aware machine
learning approaches focus on preserving the \emph{overall} classification
accuracy while improving fairness. In the presence of class-imbalance, such
methods may further aggravate the problem of discrimination by denying an
already underrepresented group (e.g., \textit{females}) the fundamental rights
of equal social privileges (e.g., equal credit opportunity).
  To this end, we propose AdaFair, a fairness-aware boosting ensemble that
changes the data distribution at each round, taking into account not only the
class errors but also the fairness-related performance of the model defined
cumulatively based on the partial ensemble. Except for the in-training boosting
of the group discriminated over each round, AdaFair directly tackles imbalance
during the post-training phase by optimizing the number of ensemble learners
for balanced error performance (BER). AdaFair can facilitate different
parity-based fairness notions and mitigate effectively discriminatory outcomes.
Our experiments show that our approach can achieve parity in terms of
statistical parity, equal opportunity, and disparate mistreatment while
maintaining good predictive performance for all classes.
