{"ID":2880896,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.14311","arxiv_id":"2508.14311","title":"Online Learning with Multiple Fairness Regularizers via Graph-Structured Feedback","abstract":"There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this work, we address this challenge in a bandit setting, where decisions are made with graph-structured feedback.","short_abstract":"There is an increasing need to enforce multiple, often competing, measures of fairness within automated decision systems. The appropriate weighting of these fairness objectives is typically unknown a priori, may change over time and, in our setting, must be learned adaptively through sequential interactions. In this wo...","url_abs":"https://arxiv.org/abs/2508.14311","url_pdf":"https://arxiv.org/pdf/2508.14311v2","authors":"[\"Quan Zhou\",\"Jakub Marecek\",\"Robert Shorten\"]","published":"2025-08-19T23:32:26Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}