CAFP: A Post-Processing Framework for Group Fairness via Counterfactual Model Averaging
This addresses fairness issues in sensitive domains like credit scoring and healthcare, offering a model-agnostic solution that is incremental over existing post-processing methods.
The paper tackles the problem of ensuring fairness in machine learning predictions by proposing CAFP, a post-processing framework that mitigates unfair influence from protected attributes without retraining the original classifier, achieving perfect demographic parity and reducing the equalized odds gap by at least half the average counterfactual bias.
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. CAFP operates by generating counterfactual versions of each input in which the sensitive attribute is flipped, and then averaging the model's predictions across factual and counterfactual instances. We provide a theoretical analysis of CAFP, showing that it eliminates direct dependence on the protected attribute, reduces mutual information between predictions and sensitive attributes, and provably bounds the distortion introduced relative to the original model. Under mild assumptions, we further show that CAFP achieves perfect demographic parity and reduces the equalized odds gap by at least half the average counterfactual bias.