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).
翻译:许多在线应用程序采用了数据驱动学习算法,其中数据随时间推移而出现,例如网络监测、股票价格预测、工作应用程序等。 基本数据分配可能随着时间推移而演变,要求随着新情况到来和旧情况过时而进行模型调整。在这种动态环境中,所谓的数据流、公平认知学习不能被视为一次性要求,而是应该包含对流的持续要求。最近的公平觉悟流分类法忽视了阶级不平衡问题,这在许多真实生活中的应用中都表现出来,并减少了歧视,主要是因为他们无法有效地学习所有类别统计,因此一般少数人的情况会“逆转”。在这项工作中,我们建议采用在线公平意识方法,在流上维持一个有效和公平的分类。 在线推进法,通过监测流的阶级不平衡和调整其决定界限,以减轻流上的歧视结果。 在8个现实世界和1个综合数据集上进行实验,而不同类别失衡,在(_____________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________