  Fairness-aware mining of massive data streams is a growing and challenging
concern in the contemporary domain of machine learning. Many stream learning
algorithms are used to replace humans at critical decision-making points e.g.,
hiring staff, assessing credit risk, etc. This calls for handling massive
incoming information with minimum response delay while ensuring fair and high
quality decisions. Recent discrimination-aware learning methods are optimized
based on overall accuracy. However, the overall accuracy is biased in favor of
the majority class; therefore, state-of-the-art methods mainly diminish
discrimination by partially or completely ignoring the minority class. In this
context, we propose a novel adaptation of Na\"ive Bayes to mitigate
discrimination embedded in the streams while maintaining high predictive
performance for both the majority and minority classes. Our proposed algorithm
is simple, fast, and attains multi-objective optimization goals. To handle
class imbalance and concept drifts, a dynamic instance weighting module is
proposed, which gives more importance to recent instances and less importance
to obsolete instances based on their membership in minority or majority class.
We conducted experiments on a range of streaming and static datasets and
deduced that our proposed methodology outperforms existing state-of-the-art
fairness-aware methods in terms of both discrimination score and balanced
accuracy.
