  Data-driven learning algorithms are employed in many online applications, in
which data become available over time, like network monitoring, stock price
prediction, job applications, etc. The underlying data distribution might
evolve over time calling for model adaptation as new instances arrive and old
instances become obsolete. In such dynamic environments, the so-called data
streams, fairness-aware learning cannot be considered as a one-off requirement,
but rather it should comprise a continual requirement over the stream. Recent
fairness-aware stream classifiers ignore the problem of class imbalance, which
manifests in many real-life applications, and mitigate discrimination mainly
because they "reject" minority instances at large due to their inability to
effectively learn all classes.
  In this work, we propose \ours, an online fairness-aware approach that
maintains a valid and fair classifier over the stream. \ours~is an online
boosting approach that changes the training distribution in an online fashion
by monitoring stream's class imbalance and tweaks its decision boundary to
mitigate discriminatory outcomes over the stream. Experiments on 8 real-world
and 1 synthetic datasets from different domains with varying class imbalance
demonstrate the superiority of our method over state-of-the-art fairness-aware
stream approaches with a range (relative) increase [11.2\%-14.2\%] in balanced
accuracy, [22.6\%-31.8\%] in gmean, [42.5\%-49.6\%] in recall, [14.3\%-25.7\%]
in kappa and [89.4\%-96.6\%] in statistical parity (fairness).
