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.

本文引入一种新颖的方法，即面向公平性自适应记忆分类器的进化多目标优化，旨在增强机器学习算法在数据流分类中的公平性。该方法通过将自适应记忆 K 最近邻算法的优势与进化多目标优化相结合，有效地处理流数据中的概念漂移，并同时最大化准确性和最小化歧视。通过在各种数据集上进行广泛实验，将提出的方法与几种基准方法在准确性和公平度指标方面进行比较，证明了该方法的有效性。结果表明，该方法具有竞争力的准确性并显著减少了歧视，突显其作为面向公平性数据流分类的稳健解决方案的潜力。进一步的分析也证实了触发进化多目标优化和适应分类器的策略在提出的方法中的有效性。