AI RESEARCH
CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging
arXiv CS.LG
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ArXi:2604.07009v1 Announce Type: cross Ensuring fairness in machine learning predictions is a critical challenge, especially when models are deployed in sensitive domains such as credit scoring, healthcare, and criminal justice. While many fairness interventions rely on data preprocessing or algorithmic constraints during