{"ID":2877724,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20021","arxiv_id":"2508.20021","title":"FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring","abstract":"Sensitive attributes like gender or age can lead to unfair predictions in machine learning tasks such as predictive business process monitoring, particularly when used without considering context. We present FairLoop1, a tool for human-guided bias mitigation in neural network-based prediction models. FairLoop distills decision trees from neural networks, allowing users to inspect and modify unfair decision logic, which is then used to fine-tune the original model towards fairer predictions. Compared to other approaches to fairness, FairLoop enables context-aware bias removal through human involvement, addressing the influence of sensitive attributes selectively rather than excluding them uniformly.","short_abstract":"Sensitive attributes like gender or age can lead to unfair predictions in machine learning tasks such as predictive business process monitoring, particularly when used without considering context. We present FairLoop1, a tool for human-guided bias mitigation in neural network-based prediction models. FairLoop distills...","url_abs":"https://arxiv.org/abs/2508.20021","url_pdf":"https://arxiv.org/pdf/2508.20021v1","authors":"[\"Felix Möhrlein\",\"Martin Käppel\",\"Julian Neuberger\",\"Sven Weinzierl\",\"Lars Ackermann\",\"Martin Matzner\",\"Stefan Jablonski\"]","published":"2025-08-27T16:30:30Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}