Enhancing Group Fairness in Online Settings Using Oblique Decision Forests

arXiv:2310.11401v2 Announce Type: replace
Abstract: 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 …

abstract arxiv context cross-entropy cs.lg decision decision forests entropy fairness forests learning systems machine machine learning processing systems type