  This paper introduces a novel approach, evolutionary multi-objective
optimisation for fairness-aware self-adjusting memory classifiers, designed to
enhance fairness in machine learning algorithms applied to data stream
classification. With the growing concern over discrimination in algorithmic
decision-making, particularly in dynamic data stream environments, there is a
need for methods that ensure fair treatment of individuals across sensitive
attributes like race or gender. The proposed approach addresses this challenge
by integrating the strengths of the self-adjusting memory K-Nearest-Neighbour
algorithm with evolutionary multi-objective optimisation. This combination
allows the new approach to efficiently manage concept drift in streaming data
and leverage the flexibility of evolutionary multi-objective optimisation to
maximise accuracy and minimise discrimination simultaneously. We demonstrate
the effectiveness of the proposed approach through extensive experiments on
various datasets, comparing its performance against several baseline methods in
terms of accuracy and fairness metrics. Our results show that the proposed
approach maintains competitive accuracy and significantly reduces
discrimination, highlighting its potential as a robust solution for
fairness-aware data stream classification. Further analyses also confirm the
effectiveness of the strategies to trigger evolutionary multi-objective
optimisation and adapt classifiers in the proposed approach.
