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.

翻译：公平性，特别是群体公平性，是机器学习系统中重要的考量因素。最常用的群体公平性增强技术是处理方法，这些方法在训练过程中混合使用公平性目标（如人口统计均等）和任务特定目标（如交叉熵）。然而，当数据以在线方式（每次一个实例）到达时，优化此类公平性目标会带来若干挑战。具体而言，群体公平性目标是通过不同人口统计群体间预测的期望来定义的。在在线设置中，算法每次只能访问单个实例，估算群体公平性目标所需额外存储空间和计算量（如前向/反向传播）远超每个时间步的任务特定目标。本文提出Aranyani——一种斜决策树集成方法——用于在线场景中做出公平决策。Aranyani的层次树结构实现了参数隔离，并使我们能够利用先前决策的聚合统计量高效计算公平性梯度，从而消除对额外存储和前向/反向传播的需求。我们还提出了一种高效的Aranyani训练框架，并从理论上分析了其若干特性。我们在5个公开基准数据集（包括视觉和语言数据集）上进行实证评估，结果表明Aranyani相比基线方法实现了更优的准确率-公平性权衡。