CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging
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 training, these approaches often require full control over the model architecture and access to protected attribute information, which may not be feasible in real-world systems. In this paper, we propose Counterfactual Averaging for Fair Predictions (CAFP), a model-agnostic post-processing method that mitigates unfair influence from protected attributes without retraining or modifying the original classifier....
| Authors | Irina Arévalo, Marcos Oliva |
|---|---|
| Primary category | cs.AI |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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