Fairness, especially group fairness, is an important consideration in the context of machine learning systems. The most commonly adopted group fairness-enhancing techniques are in-processing methods that rely on a mixture of a fairness objective (e.g., demographic parity) and a task-specific objective (e.g., cross-entropy) during the training process. However, when data arrives in an online fashion -- one instance at a time -- optimizing such fairness objectives poses several challenges. In particular, group fairness objectives are defined using expectations of predictions across different demographic groups. In the online setting, where the algorithm has access to a single instance at a time, estimating the group fairness objective requires additional storage and significantly more computation (e.g., forward/backward passes) than the task-specific objective at every time step. In this paper, we propose Aranyani, an ensemble of oblique decision trees, to make fair decisions in online settings. The hierarchical tree structure of Aranyani enables parameter isolation and allows us to efficiently compute the fairness gradients using aggregate statistics of previous decisions, eliminating the need for additional storage and forward/backward passes. We also present an efficient framework to train Aranyani and theoretically analyze several of its properties. We conduct empirical evaluations on 5 publicly available benchmarks (including vision and language datasets) to show that Aranyani achieves a better accuracy-fairness trade-off compared to baseline approaches.

在机器学习系统的背景中，尤其是在公平性方面，尤其是群体公平性，是需要考虑的一个重要因素。我们提出了一种名为Aranyani的在线设置下的公平决策方法，它采用了倾斜决策树的集合，通过利用先前决策的聚合统计量来有效计算公平梯度，从而克服了优化公平目标在在线环境中所面临的挑战，并在公开基准测试集上展示了Aranyani相对于基准方法具有更好的准确性-公平性平衡。

使用斜决策森林在在线环境中增强团体公平性