Enhancing Group Fairness in Online Settings Using Oblique Decision Forests
This addresses fairness issues in online machine learning systems, but it is incremental as it builds on existing in-processing methods with a novel tree-based approach.
The paper tackles the challenge of efficiently optimizing group fairness objectives in online learning settings, where data arrives one instance at a time, by proposing Aranyani, an ensemble of oblique decision trees. It shows that Aranyani achieves a better accuracy-fairness trade-off compared to baselines on 5 public benchmarks, though no concrete numbers are provided.
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.