Enhancing Group Fairness in Online Settings Using Oblique Decision Forests

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. Critical applications of machine learning, such as hiring (Dastin, 2022) and criminal recidivism (Larson et al., 2016), require special attention to avoid perpetuating biases present in training data (Corbett-Davies et al., 2017; Buolamwini & Gebru, 2018; Raji & Buolamwini, 2019). Group fairness, which is a well-studied paradigm for mitigating such biases in machine learning (Mehrabi et al., 2021; Hort et al., 2022), tries to achieve statistical parity of a system's predictions among different demographic (or protected) groups (e.g., gender or race). Most of these approaches rely on group fairness objectives that are optimized alongside task-specific objectives in an offline setting (Dwork et al., 2012). Group fairness objectives (e.g., demographic parity) are defined using expectations of predictions across different demographic groups, which requires the system to have access to labeled data from different groups.