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.

翻译：本文提出了一种新颖方法——面向公平感知自调整记忆分类器的进化多目标优化，旨在增强应用于数据流分类的机器学习算法的公平性。随着算法决策中歧视问题的日益关注，特别是在动态数据流环境下，亟需确保个体在种族、性别等敏感属性上受到公平对待的方法。所提方法通过整合自调整记忆K近邻算法与进化多目标优化的优势来解决这一挑战。这种结合使得新方法能够有效管理流数据中的概念漂移，同时利用进化多目标优化的灵活性，在最大化准确率的同时最小化歧视。我们通过在多种数据集上进行广泛实验，从准确率和公平性指标两方面对比了所提方法与若干基线方法的性能，从而验证了其有效性。结果表明，所提方法在保持竞争性准确率的同时显著降低了歧视，凸显了其作为数据流公平感知分类稳健解决方案的潜力。进一步分析也确认了触发进化多目标优化及自适应调整分类器策略的有效性。